Automatic data transfer between a source and a target using semantic artificial intelligence for robotic process automation

ABSTRACT

Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part (CIP) of U.S. Nonprovisionalpatent application Ser. No. 17/494,744 filed Oct. 5, 2021. The subjectmatter of this earlier filed application is hereby incorporated byreference in its entirety.

FIELD

The present invention generally relates to semantic matching, and morespecifically, to automatic data transfer between a source and a targetusing semantic artificial intelligence (AI) for robotic processautomation (RPA).

BACKGROUND

Currently, developers need to manually create robotic process automation(RPA) workflows in an RPA designer application using activities. Whilecreating the RPA workflow, the developer needs to indicate the targetgraphical element on the screen, which causes the RPA designerapplication to automatically generate a selector corresponding to thetarget element with a set of anchors. Although activity recommendationand suggestion functionality currently exists in UiPath Studio™, forexample, fully automated RPA workflow creation is not supported, nor isintuitive data transfer from a source to a target. Indicating all of thetarget graphical elements manually while creating the RPA workflow istime consuming. Accordingly, an improved and/or alternative approach maybe beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions tothe problems and needs in the art that have not yet been fullyidentified, appreciated, or solved by current RPA technologies. Forexample, some embodiments of the present invention pertain to automaticdata transfer between a source and a target using semantic AI for RPA.

In an embodiment, a non-transitory computer-readable medium stores acomputer program. The computer program is configured to cause at leastone processor to receive a selection of a source and receive a selectionof a target. The computer program is also configured to cause the atleast one processor to call one or more AI/ML models that have beentrained to perform semantic matching between labels in the source andlabels in the target, between values in the source and the labels in thetarget, or both. The computer program is further configured to cause theat least one processor to copy values from the source to the targetbased on the semantic matching between the labels in the source and thelabels in the target, between the values in the source and the labels inthe target, or both.

In another embodiment, a computer-implemented method includes calling,by a computing system, one or more AI/ML models that have been trainedto perform semantic matching between labels in a source and labels in atarget, between values in the source and the labels in the target, orboth. The computer-implemented method also includes copying values fromthe source to the target, by the computing system, based on the semanticmatching between the labels in the source and the labels in the target,between the values in the source and the labels in the target, or both.

In yet another embodiment, a computing system includes memory storingcomputer program instructions and at least one processor configured toexecute the computer program instructions. The computer programinstructions are configured to cause the at least one processor to callone or more AI/ML models that have been trained to perform semanticmatching between labels in the source and labels in the target, betweenvalues in the source and the labels in the target, or both. The computerprogram instructions are also configured to cause the at least oneprocessor to copy values from the source to the target based on thesemantic matching between the labels in the source and the labels in thetarget, between the values in the source and the labels in the target,or both. The computer program instructions are or include an RPAdesigner application or an RPA robot.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the inventionwill be readily understood, a more particular description of theinvention briefly described above will be rendered by reference tospecific embodiments that are illustrated in the appended drawings.While it should be understood that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating a hyper-automationsystem, according to an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating a deployed RPA system,according to an embodiment of the present invention.

FIG. 4 is an architectural diagram illustrating the relationship betweena designer, activities, and drivers, according to an embodiment of thepresent invention.

FIG. 5 is an architectural diagram illustrating a computing systemconfigured to perform automatic data transfer between a source and atarget using semantic AI for RPA, according to an embodiment of thepresent invention.

FIG. 6A illustrates an example of a neural network that has been trainedto recognize graphical elements in an image, according to an embodimentof the present invention.

FIG. 6B illustrates an example of a neuron, according to an embodimentof the present invention.

FIG. 7 is a flowchart illustrating a process for training AI/MLmodel(s), according to an embodiment of the present invention.

FIGS. 8A-D illustrate a matching interface for an RPA designerapplication, according to an embodiment of the present invention.

FIG. 9 illustrates an RPA designer application with an automaticallygenerated RPA workflow, according to an embodiment of the presentinvention.

FIGS. 10A-H illustrate screens of an example semantic copy and pasteinterface, according to an embodiment of the present invention.

FIG. 11 is an architectural diagram illustrating an architecture of theAI/ML models for performing semantic AI, according to an embodiment ofthe present invention.

FIG. 12 is a flowchart illustrating a process for performing automaticdata transfer between a source and a target using semantic AI for RPA atdesign time, according to an embodiment of the present invention.

FIG. 13 is a flowchart illustrating a process for performing automaticdata transfer between a source and a target using semantic AI for RPA atruntime, according to an embodiment of the present invention.

FIG. 14 is a flowchart illustrating a process for performing persistent,multi-screen automatic data transfer between a source and a target usingsemantic AI for RPA at design time, according to an embodiment of thepresent invention.

FIG. 15 is a flowchart illustrating a process for performing persistent,multi-screen automatic data transfer between a source and a target usingsemantic AI for RPA at runtime, according to an embodiment of thepresent invention.

Unless otherwise indicated, similar reference characters denotecorresponding features consistently throughout the attached drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to automatic data transfer between a source anda target using semantic AI for RPA. Most manual tasks can be reduced togoing to a source, going field by field, and putting the information ina destination (e.g., inputting the contents of an invoice into a billingsystem). Some embodiments provide a user with the option of selecting asource and a target and indicating through an intuitive user interfacethat he or she would like to copy data from the source to thedestination, regardless of format. For instance, the source and/ortarget may be a web page, a graphical user interface (GUI) of anapplication, an image, a file explorer, a spreadsheet, a relationaldatabase, a flat file source, any other suitable format, or anycombination thereof. In some embodiments, the source and target may havedifferent formats, such as the source being a PDF and the target being auser interface of a billing application. In certain embodiments, thesource, target, or both may not be visible to the user.

In some embodiments, AI/ML model(s) may be used to extract theinformation from the source (e.g., key-value pairs, labels andassociated values, etc.) and target (e.g., labels and text fieldscorresponding to those labels). The AI/ML model(s) may include, but arenot limited to, a computer vision (CV) model, an optical characterrecognition (OCR) module, a document processing module, etc. A semanticmatching module is then employed to map labels and values in the sourceto labels and associated fields, UI elements, file locations, or anyother suitable location to copy the values without deviating from thescope of the invention. Once mapped, the values are copied automaticallyfrom the source to the destination.

In some embodiments, if a label with a value in the source is notmatched to a label in the target with at least a certain matchingthreshold (e.g., 75%, 90%, etc.), various actions may be taken. Forinstance, the label and its value from the source may be ignored.Alternatively, the user may be prompted to identify whether acorresponding label exists in the target and to provide theidentification. In certain embodiments, the user may be shown a list ofpotential matching candidates that have a confidence value that is lessthan the matching threshold, but more than a display threshold (e.g.,35%, 50%, etc.).

In some embodiments, target-based schema identification may be employed.When looking at a source, such as a document, a web form, a userinterface of a software application, a data file, etc., it is oftendifficult for software to determine which fields are labels and whichare values associated with those labels. Values may include, but are notlimited to, text strings, numbers, etc. However, a target templatedocument, web form, user interface of a software application, etc.lacking such values may be analyzed to detect labels therein. Sincevalues have not yet been entered for various labels (e.g., first name,company, customer number, etc.), these labels are easier to detect thanwhen the target also includes various values associated with the labels.

However, in some embodiments, the source may not have a schema per-se.In such cases, a natural language processing (NLP) model may beemployed. For instance, GPT-3 may be used, which is an autoregressivelanguage model that uses deep learning to produce human-like text. Theinputs to the NLP model may be the source document and a description ofwhat the user wants to extract (e.g., in the form of a paragraph oftext) in plain English. The source document may then be analyzed andinformation therein may be extracted using the NLP model based on thisinput. Semantic understanding is applied to the text of the document asa whole by the NLP model to “guess” which information is desired.

Human language may be separated into fragments and analyze thegrammatical structure of sentences and the meaning of words in context.This helps software employing NLP to read and understand spoken orwritten text in a similar manner to humans. Sentences may be broken downinto tokens, which are smaller semantic units or fragments, viatokenization. Parts of speech may be tagged as well, such as markingwords as nouns, verbs, adjectives, adverbs, pronouns, etc. Stemming andlemmatization may be used to standardize words by reducing them to theirroot forms. Also, stop words may be removed. These are common words thatadd little or no unique information, such as prepositions and articles(e.g., at, to, a, the, etc.). The NLP model can then be run on thisinformation to extract useful information.

Some embodiments may be attended or unattended, as described in furtherdetail with respect to FIGS. 1 and 2 below. For attended embodiments, insome aspects, if a user clicks on a field because its value isincorrect, other scored options may appear for the user to select.Similarity of candidates may be ranked using any suitable similaritymeasure and/or measure for an amount of mismatch between two valueswithout deviating from the scope of the invention. For instance, invarious embodiments, the similarity threshold may represent a maximumamount of mismatch or a minimum amount of similarity required for amatch. The similarity measure may be expressed in various ways, such asaccording to an inter-string distance known as a “string metric.” Oneexample string metric known as the Levenshtein distance determines acount of operations necessary to transform one string into the other.Other inter-string distances include the Hamming distance and theJaro-Winkler distance, among others.

Depending on the chosen manner of computing the similarity measure, thesimilarity threshold can have various interpretations. For instance, thesimilarity threshold may indicate a maximum count of characters that candiffer between the two strings or a fractional degree of mismatchcalculated as a proportion of the total count of characters (e.g.,combined string length). In some embodiments, the similarity thresholdmay be re-scaled to a predetermined interval, such as between 0 and 1,between 0 and 100, between 7 and 34, etc. In one nonlimiting example, arelatively high similarity threshold (e.g., close to 1 or 100%)indicates a requirement for an almost exact match, i.e., the value ofthe fuzzy attribute in the runtime target is only allowed to depart veryslightly from the value of the respective attribute in the design timetarget. In contrast, when the similarity threshold is relatively low(e.g., close to 0), almost any values of the respective fuzzy attributeare considered as matching. Some allow adjusting the similaritythreshold at design time, for instance by way of a slider.

Whereas existing techniques use multiple or many source examples tolearn key-value pairs from the source, some embodiments essentiallyoperate in reverse. Labels are determined from an empty target. Per theabove, the source and the target can have different types. For instance,one may be a web page while the other is an Excel® spreadsheet. Evenimages of a graphical user interface (GUI) could be used by employingcomputer vision (CV), optical character recognition (OCR), and/or adocument processing framework. See, for example, U.S. Patent ApplicationPublication No. 2021/0097274, the subject matter of which is herebyincorporated by reference in its entirety. Such techniques can be usedto classify the source and/or the target.

Users may provide training information for building libraries forcertain types of targets. For instance, CV, OCR, and document processingartificial intelligence (AI)/machine learning (ML) models may beprovided “out of the box” that are capable of achieving an accuracy of70%. As users provide corrections for incorrect values and/orlabel-value associations, these may be used to retrain the respectiveAI/ML model, increasing its accuracy.

In some embodiments, analytics may be performed on the user interactionswith the software to determine tasks that are used the most. These maybe reported to a Center of Excellence (COE) and used as a form of taskmining. Using current task mining technologies, it is difficult toidentify the start and end of certain repetitive tasks. However, byanalyzing the way that the user performs a copy and paste operation,some embodiments can identify where the task started and where it ended.“Copy” could signify the start of a copy-and-paste task and “paste”could signify the end of such a task. The actions performed in betweenthe copy and the paste can be included as a flow of tasks in a workflow.Furthermore, a reliable and effective workflow that can be created usingthis information.

In certain embodiments, both local and global AI/ML models may beincluded. For instance, the local AI/ML model may learn preferences of agiven user while the global AI/ML model learns collective preferencesfrom many or all users. A threshold may be required to use a result froman AI/ML model. For instance, if the local model is employed first anddoes not meet the suggestion threshold for one or more attributes, theglobal model may be tried to see if it comes up with a useful result.

In order to perform target-based schema identification, a user mayselect a target that does not yet have values. Using CV, OCR, and/ordocument processing AI/ML models, the labels, or “keys,” are determinedin the target, as well as their locations. This allows the system todetermine the type of the target. For instance, a web form tends to haverectangular text fields to the right of the respective label for thattext field. An invoice will tend to have certain fields, such ascustomer number, the word “invoice,” some variation of “amount,” etc.The location(s) of the labels may also be used to assist in thedetermination. For instance, text fields often tend to be arrangedvertically with one above and one below, except at the top or bottom ofthe column of text fields. Anchor or multi-anchor-based extraction maybe performed in some embodiments, such as using the techniques disclosedin U.S. Pat. No. 10,936,351.

In some embodiments, the source data or source screen and the targetscreen are selected on a matching interface, and label and schemaidentification, semantic matching, and transferring (e.g.,copy-and-pasting) the data for matched labels from the source to thetarget is performed automatically without further action by the user.Such functionality may be provided at design time for RPA developers(including citizen developers) and at runtime for end users. In somedesign time embodiments, one or more RPA workflow activities areautomatically created based on the semantic mapping that can be executedto perform the semantic AI functionality as part of an automationexecuted by an RPA robot.

In some embodiments, a list of data fields may be obtained, such as froman Excel® spreadsheet, a relational database, a flat file source, etc.The semantic matching AI/ML model may iterate over the entries in thesource data and match them to labels and corresponding data entrylocations in the target. The semantic matching AI/ML model may betrained to do this regardless of the type of the data source. Due to thesemantic matching functionality of this AI/ML model, a 1-to-1 matchingmay not be required. For instance, a natural language model may seek tomatch identical or similar names/phrases in a target screen to those inthe source data (or start with the source data and look for similarnames/phrases in the target screen). In certain embodiments, anextensive set of training data is used to make the semantic matchingAI/ML model more accurate since there may be many similar words orphrases for certain terms and there may also be many different subsetsdepending on context. In some embodiments, context may also be used. Forinstance, the semantic matching AI/ML model may learn that a giventarget pertains to banking details vs. an invoice, vs. a purchase ordervs. contact information, etc.

The semantic matching AI/ML model of some embodiments may be deployed toassist RPA developers during design time. However, in some embodiments,the semantic matching AI/ML model may be used at runtime to provide morerobust functionality and self-healing. This may be employed if the UIdescriptor fails to identify the target graphical element at runtimerather than generally in some embodiments since UI descriptors tend tobe considerably faster than semantic matching AI/ML models. As such, UIdescriptors should be employed first for the same or similar targetscreens.

For instance, if a given target element cannot be identified by a givenuser interface (UI) descriptor at runtime, such as if a UI changes dueto a new version of a target application, the semantic matching AI/MLmodel may be used to attempt to identify the target graphical element.This information may then be added as a synonym for the word or phraseof interest, and the UI descriptor for that graphical element may beupdated such that the UI descriptor will work going forward. If the userinterface changes yet again and the changed graphical element and/oranchor(s) are similar enough, the RPA robot may be able to identify thetarget graphical element in the new version of the application. See U.S.Patent Application Publication No. 2022/0012024, for example.

A UI descriptor is a set of instructions for finding a UI element. UIdescriptors in some embodiments are an encapsulated data/struct formatthat includes UI element selector(s), anchor selector(s), computervision (CV) descriptor(s), unified target descriptor(s), a screen imagecapture (context), an element image capture, other metadata (e.g., theapplication and application version), a combination thereof, etc. Theencapsulated data/struct format may be extensible with future updates tothe platform and is not limited to the above definition. Any suitable UIdescriptor for identifying a UI element on a screen may be used withoutdeviating from the scope of the invention.

In some embodiments, what the semantic matching AI/ML model is detectingmay be combined with unified target descriptors for runtime detection.For such embodiments, in addition to words and phrases for the sourceand target, once mappings are confirmed, unified target information maybe collected. At runtime, the unified target descriptor may be triedfirst, and if not successful, the semantic matching AI/ML model may beused.

Unified target descriptors tend to be more stable and accurate thanAI/ML models. A unified target descriptor chains together multiple typesof UI descriptors. Unified target information includes UI descriptorinformation that facilitates identification of graphical elements forthe UI descriptor(s) that are employed.

A unified target descriptor may function like a finite state machine(FSM), where in a first context, a first UI descriptor mechanism isapplied, in a second context, a second UI descriptor is applied, etc. Inother words, the UI descriptors may work with a unified target thatencompasses some or all UI detection mechanisms through which imagedetection and definition are performed in some embodiments. The unifiedtarget may merge multiple techniques of identifying and automating UIelements into a single cohesive approach. The unified target mayprioritize certain UI descriptor types in some embodiments, such asprioritizing selector-based and driver-based UI detection mechanisms andusing CV as a fallback to find a target UI element if the first twomechanisms are not successful.

In some embodiments, an NLP AI/ML model may be used in addition to or inlieu of a semantic matching AI/ML model. In certain embodiments, theseAI/ML models may be used together. For instance, of one of the modelsmeets or exceeds a certain threshold, if the average of both of themodels meets or exceeds a threshold, etc., a match may be proposed tothe user.

In some embodiments, feedback loop functionality is provided. Forinstance, if a user adds a match or corrects a match proposed by thesemantic matching AI/ML model, information pertaining to this match maybe saved. This information may include, but is not limited to, ascreenshot of the target application, the label in the targetapplication and the corresponding label in the source screen or sourcedata, the label of the incorrect match, etc. The context may also becaptured, such as that the correction occurred for a webpage, SAP®, etc.This data may be used in conjunction with other labeled data collectedin this manner to retrain the semantic matching AI/ML model.

FIG. 1 is an architectural diagram illustrating a hyper-automationsystem 100, according to an embodiment of the present invention.“Hyper-automation,” as used herein, refers to automation systems thatbring together components of process automation, integration tools, andtechnologies that amplify the ability to automate work. For instance,RPA may be used at the core of a hyper-automation system in someembodiments, and in certain embodiments, automation capabilities may beexpanded with artificial intelligence (AI)/machine learning (ML),process mining, analytics, and/or other advanced tools. As thehyper-automation system learns processes, trains AI/ML models, andemploys analytics, for example, more and more knowledge work may beautomated, and computing systems in an organization, e.g., both thoseused by individuals and those that run autonomously, may all be engagedto be participants in the hyper-automation process. Hyper-automationsystems of some embodiments allow users and organizations to efficientlyand effectively discover, understand, and scale automations.

Hyper-automation system 100 includes user computing systems, such asdesktop computer 102, tablet 104, and smart phone 106. However, anydesired user computing system may be used without deviating from thescope of the invention including, but not limited to, smart watches,laptop computers, servers, Internet-of-Things (IoT) devices, etc. Also,while three user computing systems are shown in FIG. 1 , any suitablenumber of user computing systems may be used without deviating from thescope of the invention. For instance, in some embodiments, dozens,hundreds, thousands, or millions of user computing systems may be used.The user computing systems may be actively used by a user or runautomatically without much or any user input.

Each user computing system 102, 104, 106 has respective automationprocess(es) 110, 112, 114 running thereon. Automation process(es) 110,112, 114 may include, but are not limited to, RPA robots, part of anoperating system, downloadable application(s) for the respectivecomputing system, any other suitable software and/or hardware, or anycombination of these without deviating from the scope of the invention.In some embodiments, one or more of process(es) 110, 112, 114 may belisteners. Listeners may be RPA robots, part of an operating system, adownloadable application for the respective computing system, or anyother software and/or hardware without deviating from the scope of theinvention. Indeed, in some embodiments, the logic of the listener(s) isimplemented partially or completely via physical hardware.

Listeners monitor and record data pertaining to user interactions withrespective computing systems and/or operations of unattended computingsystems and send the data to a core hyper-automation system 120 via anetwork (e.g., a local area network (LAN), a mobile communicationsnetwork, a satellite communications network, the Internet, anycombination thereof, etc.). The data may include, but is not limited to,which buttons were clicked, where a mouse was moved, the text that wasentered in a field, that one window was minimized and another wasopened, the application associated with a window, etc. In certainembodiments, the data from the listeners may be sent periodically aspart of a heartbeat message. In some embodiments, the data may be sentto core hyper-automation system 120 once a predetermined amount of datahas been collected, after a predetermined time period has elapsed, orboth. One or more servers, such as server 130, receive and store datafrom the listeners in a database, such as database 140.

Automation processes may execute the logic developed in workflows duringdesign time. In the case of RPA, workflows may include a set of steps,defined herein as “activities,” that are executed in a sequence or someother logical flow. Each activity may include an action, such asclicking a button, reading a file, writing to a log panel, etc. In someembodiments, workflows may be nested or embedded.

Long-running workflows for RPA in some embodiments are master projectsthat support service orchestration, human intervention, and long-runningtransactions in unattended environments. See, for example, U.S. Pat. No.10,860,905. Human intervention comes into play when certain processesrequire human inputs to handle exceptions, approvals, or validationbefore proceeding to the next step in the activity. In this situation,the process execution is suspended, freeing up the RPA robots until thehuman task completes.

A long-running workflow may support workflow fragmentation viapersistence activities and may be combined with invoke process andnon-user interaction activities, orchestrating human tasks with RPArobot tasks. In some embodiments, multiple or many computing systems mayparticipate in executing the logic of a long-running workflow. Thelong-running workflow may run in a session to facilitate speedyexecution. In some embodiments, long-running workflows may orchestratebackground processes that may contain activities performing ApplicationProgramming Interface (API) calls and running in the long-runningworkflow session. These activities may be invoked by an invoke processactivity in some embodiments. A process with user interaction activitiesthat runs in a user session may be called by starting a job from aconductor activity (conductor described in more detail later herein).The user may interact through tasks that require forms to be completedin the conductor in some embodiments. Activities may be included thatcause the RPA robot to wait for a form task to be completed and thenresume the long-running workflow.

One or more of automation process(es) 110, 112, 114 is in communicationwith core hyper-automation system 120. In some embodiments, corehyper-automation system 120 may run a conductor application on one ormore servers, such as server 130. While one server 130 is shown forillustration purposes, multiple or many servers that are proximate toone another or in a distributed architecture may be employed withoutdeviating from the scope of the invention. For instance, one or moreservers may be provided for conductor functionality, AI/ML modelserving, authentication, governance, and/or any other suitablefunctionality without deviating from the scope of the invention. In someembodiments, core hyper-automation system 120 may incorporate or be partof a public cloud architecture, a private cloud architecture, a hybridcloud architecture, etc. In certain embodiments, core hyper-automationsystem 120 may host multiple software-based servers on one or morecomputing systems, such as server 130. In some embodiments, one or moreservers of core hyper-automation system 120, such as server 130, may beimplemented via one or more virtual machines (VMs).

In some embodiments, one or more of automation process(es) 110, 112, 114may call one or more AI/ML models 132 deployed on or accessible by corehyper-automation system 120. AI/ML models 132 may be trained for anysuitable purpose without deviating from the scope of the invention, aswill be discussed in more detail later herein. Two or more of AI/MLmodels 132 may be chained in some embodiments (e.g., in series, inparallel, or a combination thereof) such that they collectively providecollaborative output(s). AI/ML models 132 may perform or assist withcomputer vision (CV), optical character recognition (OCR), documentprocessing and/or understanding, semantic learning and/or analysis,analytical predictions, process discovery, task mining, testing,automatic RPA workflow generation, sequence extraction, clusteringdetection, audio-to-text translation, any combination thereof, etc.However, any desired number and/or type(s) of AI/ML models may be usedwithout deviating from the scope of the invention. Using multiple AI/MLmodels may allow the system to develop a global picture of what ishappening on a given computing system, for example. For instance, oneAI/ML model could perform OCR, another could detect buttons, anothercould compare sequences, etc. Patterns may be determined individually byan AI/ML model or collectively by multiple AI/ML models. In certainembodiments, one or more AI/ML models are deployed locally on at leastone of computing systems 102, 104, 106.

In some embodiments, multiple AI/ML models 132 may be used. Each AI/MLmodel 132 is an algorithm (or model) that runs on the data, and theAI/ML model itself may be a deep learning neural network (DLNN) oftrained artificial “neurons” that are trained on training data, forexample. In some embodiments, AI/ML models 132 may have multiple layersthat perform various functions, such as statistical modeling (e.g.,hidden Markov models (HMMs)), and utilize deep learning techniques(e.g., long short term memory (LSTM) deep learning, encoding of previoushidden states, etc.) to perform the desired functionality.

Hyper-automation system 100 may provide four main groups offunctionality in some embodiments: (1) discovery; (2) buildingautomations; (3) management; and (4) engagement. Automations (e.g., runon a user computing system, a server, etc.) may be run by softwarerobots, such as RPA robots, in some embodiments. For instance, attendedrobots, unattended robots, and/or test robots may be used. Attendedrobots work with users to assist them with tasks (e.g., via UiPathAssistant™). Unattended robots work independently of users and may runin the background, potentially without user knowledge. Test robots areunattended robots that run test cases against applications or RPAworkflows. Test robots may be run on multiple computing systems inparallel in some embodiments.

The discovery functionality may discover and provide automaticrecommendations for different opportunities of automations of businessprocesses. Such functionality may be implemented by one or more servers,such as server 130. The discovery functionality may include providing anautomation hub, process mining, task mining, and/or task capture in someembodiments. The automation hub (e.g., UiPath Automation Hub™) mayprovide a mechanism for managing automation rollout with visibility andcontrol. Automation ideas may be crowdsourced from employees via asubmission form, for example. Feasibility and return on investment (ROI)calculations for automating these ideas may be provided, documentationfor future automations may be collected, and collaboration may beprovided to get from automation discovery to build-out faster.

Process mining (e.g., via UiPath Automation Cloud™ and/or UiPath AICenter™) refers to the process of gathering and analyzing the data fromapplications (e.g., enterprise resource planning (ERP) applications,customer relation management (CRM) applications, email applications,call center applications, etc.) to identify what end-to-end processesexist in an organization and how to automate them effectively, as wellas indicate what the impact of the automation will be. This data may begleaned from user computing systems 102, 104, 106 by listeners, forexample, and processed by servers, such as server 130. One or more AI/MLmodels 132 may be employed for this purpose in some embodiments. Thisinformation may be exported to the automation hub to speed upimplementation and avoid manual information transfer. The goal ofprocess mining may be to increase business value by automating processeswithin an organization. Some examples of process mining goals include,but are not limited to, increasing profit, improving customersatisfaction, regulatory and/or contractual compliance, improvingemployee efficiency, etc.

Task mining (e.g., via UiPath Automation Cloud™ and/or UiPath AICenter™) identifies and aggregates workflows (e.g., employee workflows),and then applies AI to expose patterns and variations in day-to-daytasks, scoring such tasks for ease of automation and potential savings(e.g., time and/or cost savings). One or more AI/ML models 132 may beemployed to uncover recurring task patterns in the data. Repetitivetasks that are ripe for automation may then be identified. Thisinformation may initially be provided by listeners and analyzed onservers of core hyper-automation system 120, such as server 130, in someembodiments. The findings from task mining (e.g., Extensible ApplicationMarkup Language (XAML) process data) may be exported to processdocuments or to a designer application such as UiPath Studio™ to createand deploy automations more rapidly.

Task mining in some embodiments may include taking screenshots with useractions (e.g., mouse click locations, keyboard inputs, applicationwindows and graphical elements the user was interacting with, timestampsfor the interactions, etc.), collecting statistical data (e.g.,execution time, number of actions, text entries, etc.), editing andannotating screenshots, specifying types of actions to be recorded, etc.

Task capture (e.g., via UiPath Automation Cloud™ and/or UiPath AICenter™) automatically documents attended processes as users work orprovides a framework for unattended processes. Such documentation mayinclude desired tasks to automate in the form of process definitiondocuments (PDDs), skeletal workflows, capturing actions for each part ofa process, recording user actions and automatically generating acomprehensive workflow diagram including the details about each step,Microsoft Word® documents, XAML files, and the like. Build-readyworkflows may be exported directly to a designer application in someembodiments, such as UiPath Studio™. Task capture may simplify therequirements gathering process for both subject matter expertsexplaining a process and Center of Excellence (CoE) members providingproduction-grade automations.

Building automations may be accomplished via a designer application(e.g., UiPath Studio™, UiPath StudioX™, or UiPath Web™). For instance,RPA developers of an RPA development facility 150 may use RPA designerapplications 154 of computing systems 152 to build and test automationsfor various applications and environments, such as web, mobile, SAP®,and virtualized desktops. API integration may be provided for variousapplications, technologies, and platforms. Predefined activities,drag-and-drop modeling, and a workflow recorder, may make automationeasier with minimal coding. Document understanding functionality may beprovided via Drag-and-drop AI skills for data extraction andinterpretation that call one or more AI/ML models 132. Such automationsmay process virtually any document type and format, including tables,checkboxes, signatures, and handwriting. When data is validated orexceptions are handled, this information may be used to retrain therespective AI/ML models, improving their accuracy over time.

An integration service may allow developers to seamlessly combine userinterface (UI) automation with API automation, for example. Automationsmay be built that require APIs or traverse both API and non-APIapplications and systems. A repository (e.g., UiPath Object Repository™)or marketplace (e.g., UiPath Marketplace™) for pre-built RPA and AItemplates and solutions may be provided to allow developers to automatea wide variety of processes more quickly. Thus, when buildingautomations, hyper-automation system 100 may provide user interfaces,development environments, API integration, pre-built and/or custom-builtAI/ML models, development templates, integrated development environments(IDEs), and advanced AI capabilities. Hyper-automation system 100enables development, deployment, management, configuration, monitoring,debugging, and maintenance of RPA robots in some embodiments, which mayprovide automations for hyper-automation system 100.

In some embodiments, components of hyper-automation system 100, such asdesigner application(s) and/or an external rules engine, provide supportfor managing and enforcing governance policies for controlling variousfunctionality provided by hyper-automation system 100. Governance is theability for organizations to put policies in place to prevent users fromdeveloping automations (e.g., RPA robots) capable of taking actions thatmay harm the organization, such as violating the E.U. General DataProtection Regulation (GDPR), the U.S. Health Insurance Portability andAccountability Act (HIPAA), third party application terms of service,etc. Since developers may otherwise create automations that violateprivacy laws, terms of service, etc. while performing their automations,some embodiments implement access control and governance restrictions atthe robot and/or robot design application level. This may provide anadded level of security and compliance into to the automation processdevelopment pipeline in some embodiments by preventing developers fromtaking dependencies on unapproved software libraries that may eitherintroduce security risks or work in a way that violates policies,regulations, privacy laws, and/or privacy policies. See, for example,U.S. Nonprovisional patent application Ser. No. 16/924,499 (published asU.S. Patent Application Publication No. 2022/0011732), which is herebyincorporated by reference in its entirety.

The management functionality may provide management, deployment, andoptimization of automations across an organization. The managementfunctionality may include orchestration, test management, AIfunctionality, and/or insights in some embodiments. Managementfunctionality of hyper-automation system 100 may also act as anintegration point with third-party solutions and applications forautomation applications and/or RPA robots. The management capabilitiesof hyper-automation system 100 may include, but are not limited to,facilitating provisioning, deployment, configuration, queuing,monitoring, logging, and interconnectivity of RPA robots, among otherthings.

A conductor application, such as UiPath Orchestrator™ (which may beprovided as part of the UiPath Automation Cloud™ in some embodiments, oron premises, in VMs, in a private or public cloud, in a Linux™ VM, or asa cloud native single container suite via UiPath Automation Suite™),provides orchestration capabilities to deploy, monitor, optimize, scale,and ensure security of RPA robot deployments. A test suite (e.g., UiPathTest Suite™) may provide test management to monitor the quality ofdeployed automations. The test suite may facilitate test planning andexecution, meeting of requirements, and defect traceability. The testsuite may include comprehensive test reporting.

Analytics software (e.g., UiPath Insights™) may track, measure, andmanage the performance of deployed automations. The analytics softwaremay align automation operations with specific key performance indicators(KPIs) and strategic outcomes for an organization. The analyticssoftware may present results in a dashboard format for betterunderstanding by human users.

A data service (e.g., UiPath Data Service™) may be stored in database140, for example, and bring data into a single, scalable, secure placewith a drag-and-drop storage interface. Some embodiments may providelow-code or no-code data modeling and storage to automations whileensuring seamless access, enterprise-grade security, and scalability ofthe data. AI functionality may be provided by an AI center (e.g., UiPathAI Center™), which facilitates incorporation of AI/ML models intoautomations. Pre-built AI/ML models, model templates, and variousdeployment options may make such functionality accessible even to thosewho are not data scientists. Deployed automations (e.g., RPA robots) maycall AI/ML models from the AI center, such as AI/ML models 132.Performance of the AI/ML models may be monitored, and be trained andimproved using human-validated data, such as that provided by datareview center 160. Human reviewers may provide labeled data to corehyper-automation system 120 via a review application 152 on computingsystems 154. For instance, human reviewers may validate that predictionsby AI/ML models 132 are accurate or provide corrections otherwise. Thisdynamic input may then be saved as training data for retraining AI/MLmodels 132, and may be stored in a database such as database 140, forexample. The AI center may then schedule and execute training jobs totrain the new versions of the AI/ML models using the training data. Bothpositive and negative examples may be stored and used for retraining ofAI/ML models 132.

The engagement functionality engages humans and automations as one teamfor seamless collaboration on desired processes. Low-code applicationsmay be built (e.g., via UiPath Apps™) to connect browser tabs and legacysoftware, even that lacking APIs in some embodiments. Applications maybe created quickly using a web browser through a rich library ofdrag-and-drop controls, for instance. An application can be connected toa single automation or multiple automations.

An action center (e.g., UiPath Action Center™) provides astraightforward and efficient mechanism to hand off processes fromautomations to humans, and vice versa. Humans may provide approvals orescalations, make exceptions, etc. The automation may then perform theautomatic functionality of a given workflow.

A local assistant may be provided as a launchpad for users to launchautomations (e.g., UiPath Assistant™). This functionality may beprovided in a tray provided by an operating system, for example, and mayallow users to interact with RPA robots and RPA robot-poweredapplications on their computing systems. An interface may listautomations approved for a given user and allow the user to run them.These may include ready-to-go automations from an automationmarketplace, an internal automation store in an automation hub, etc.When automations run, they may run as a local instance in parallel withother processes on the computing system so users can use the computingsystem while the automation performs its actions. In certainembodiments, the assistant is integrated with the task capturefunctionality such that users can document their soon-to-be-automatedprocesses from the assistant launchpad.

Chatbots (e.g., UiPath Chatbots™), social messaging applications, an/orvoice commands may enable users to run automations. This may simplifyaccess to information, tools, and resources users need in order tointeract with customers or perform other activities. Conversationsbetween people may be readily automated, as with other processes.Trigger RPA robots kicked off in this manner may perform operations suchas checking an order status, posting data in a CRM, etc., potentiallyusing plain language commands.

End-to-end measurement and government of an automation program at anyscale may be provided by hyper-automation system 100 in someembodiments. Per the above, analytics may be employed to understand theperformance of automations (e.g., via UiPath Insights™). Data modelingand analytics using any combination of available business metrics andoperational insights may be used for various automated processes.Custom-designed and pre-built dashboards allow data to be visualizedacross desired metrics, new analytical insights to be discovered,performance indicators to be tracked, ROI to be discovered forautomations, telemetry monitoring to be performed on user computingsystems, errors and anomalies to be detected, and automations to bedebugged. An automation management console (e.g., UiPath AutomationOps™) may be provided to manage automations throughout the automationlifecycle. An organization may govern how automations are built, whatusers can do with them, and which automations users can access.

Hyper-automation system 100 provides an iterative platform in someembodiments. Processes can be discovered, automations can be built,tested, and deployed, performance may be measured, use of theautomations may readily be provided to users, feedback may be obtained,AI/ML models may be trained and retrained, and the process may repeatitself. This facilitates a more robust and effective suite ofautomations.

FIG. 2 is an architectural diagram illustrating an RPA system 200,according to an embodiment of the present invention. In someembodiments, RPA system 200 is part of hyper-automation system 100 ofFIG. 1 . RPA system 200 includes a designer 210 that allows a developerto design and implement workflows. Designer 210 may provide a solutionfor application integration, as well as automating third-partyapplications, administrative Information Technology (IT) tasks, andbusiness IT processes. Designer 210 may facilitate development of anautomation project, which is a graphical representation of a businessprocess. Simply put, designer 210 facilitates the development anddeployment of workflows and robots. In some embodiments, designer 210may be an application that runs on a user's desktop, an application thatruns remotely in a VM, a web application, etc.

The automation project enables automation of rule-based processes bygiving the developer control of the execution order and the relationshipbetween a custom set of steps developed in a workflow, defined herein as“activities” per the above. One commercial example of an embodiment ofdesigner 210 is UiPath Studio™. Each activity may include an action,such as clicking a button, reading a file, writing to a log panel, etc.In some embodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences,flowcharts, Finite State Machines (FSMs), and/or global exceptionhandlers. Sequences may be particularly suitable for linear processes,enabling flow from one activity to another without cluttering aworkflow. Flowcharts may be particularly suitable to more complexbusiness logic, enabling integration of decisions and connection ofactivities in a more diverse manner through multiple branching logicoperators. FSMs may be particularly suitable for large workflows. FSMsmay use a finite number of states in their execution, which aretriggered by a condition (i.e., transition) or an activity. Globalexception handlers may be particularly suitable for determining workflowbehavior when encountering an execution error and for debuggingprocesses.

Once a workflow is developed in designer 210, execution of businessprocesses is orchestrated by conductor 220, which orchestrates one ormore robots 230 that execute the workflows developed in designer 210.One commercial example of an embodiment of conductor 220 is UiPathOrchestrator™. Conductor 220 facilitates management of the creation,monitoring, and deployment of resources in an environment. Conductor 220may act as an integration point with third-party solutions andapplications. Per the above, in some embodiments, conductor 220 may bepart of core hyper-automation system 120 of FIG. 1 .

Conductor 220 may manage a fleet of robots 230, connecting and executingrobots 230 from a centralized point. Types of robots 230 that may bemanaged include, but are not limited to, attended robots 232, unattendedrobots 234, development robots (similar to unattended robots 234, butused for development and testing purposes), and nonproduction robots(similar to attended robots 232, but used for development and testingpurposes). Attended robots 232 are triggered by user events and operatealongside a human on the same computing system. Attended robots 232 maybe used with conductor 220 for a centralized process deployment andlogging medium. Attended robots 232 may help the human user accomplishvarious tasks, and may be triggered by user events. In some embodiments,processes cannot be started from conductor 220 on this type of robotand/or they cannot run under a locked screen. In certain embodiments,attended robots 232 can only be started from a robot tray or from acommand prompt. Attended robots 232 should run under human supervisionin some embodiments.

Unattended robots 234 run unattended in virtual environments and canautomate many processes. Unattended robots 234 may be responsible forremote execution, monitoring, scheduling, and providing support for workqueues. Debugging for all robot types may be run in designer 210 in someembodiments. Both attended and unattended robots may automate varioussystems and applications including, but not limited to, mainframes, webapplications, VMs, enterprise applications (e.g., those produced bySAP®, SalesForce®, Oracle®, etc.), and computing system applications(e.g., desktop and laptop applications, mobile device applications,wearable computer applications, etc.).

Conductor 220 may have various capabilities including, but not limitedto, provisioning, deployment, configuration, queueing, monitoring,logging, and/or providing interconnectivity. Provisioning may includecreating and maintenance of connections between robots 230 and conductor220 (e.g., a web application). Deployment may include assuring thecorrect delivery of package versions to as signed robots 230 forexecution. Configuration may include maintenance and delivery of robotenvironments and process configurations. Queueing may include providingmanagement of queues and queue items. Monitoring may include keepingtrack of robot identification data and maintaining user permissions.Logging may include storing and indexing logs to a database (e.g., astructured query language (SQL) database or a “not only” SQL (NoSQL)database) and/or another storage mechanism (e.g., Elastic Search®, whichprovides the ability to store and quickly query large datasets).Conductor 220 may provide interconnectivity by acting as the centralizedpoint of communication for third-party solutions and/or applications.

Robots 230 are execution agents that implement workflows built indesigner 210. One commercial example of some embodiments of robot(s) 230is UiPath Robots™. In some embodiments, robots 230 install the MicrosoftWindows® Service Control Manager (SCM)-managed service by default. As aresult, such robots 230 can open interactive Windows® sessions under thelocal system account, and have the rights of a Windows® service.

In some embodiments, robots 230 can be installed in a user mode. Forsuch robots 230, this means they have the same rights as the user underwhich a given robot 230 has been installed. This feature may also beavailable for High Density (HD) robots, which ensure full utilization ofeach machine at its maximum potential. In some embodiments, any type ofrobot 230 may be configured in an HD environment.

Robots 230 in some embodiments are split into several components, eachbeing dedicated to a particular automation task. The robot components insome embodiments include, but are not limited to, SCM-managed robotservices, user mode robot services, executors, agents, and command line.SCM-managed robot services manage and monitor Windows® sessions and actas a proxy between conductor 220 and the execution hosts (i.e., thecomputing systems on which robots 230 are executed). These services aretrusted with and manage the credentials for robots 230. A consoleapplication is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows®sessions and act as a proxy between conductor 220 and the executionhosts. User mode robot services may be trusted with and manage thecredentials for robots 230. A Windows® application may automatically belaunched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they mayexecute workflows. Executors may be aware of per-monitor dots per inch(DPI) settings. Agents may be Windows® Presentation Foundation (WPF)applications that display the available jobs in the system tray window.Agents may be a client of the service. Agents may request to start orstop jobs and change settings. The command line is a client of theservice. The command line is a console application that can request tostart jobs and waits for their output.

Having components of robots 230 split as explained above helpsdevelopers, support users, and computing systems more easily run,identify, and track what each component is executing. Special behaviorsmay be configured per component this way, such as setting up differentfirewall rules for the executor and the service. The executor may alwaysbe aware of DPI settings per monitor in some embodiments. As a result,workflows may be executed at any DPI, regardless of the configuration ofthe computing system on which they were created. Projects from designer210 may also be independent of browser zoom level in some embodiments.For applications that are DPI-unaware or intentionally marked asunaware, DPI may be disabled in some embodiments.

RPA system 200 in this embodiment is part of a hyper-automation system.Developers may use designer 210 to build and test RPA robots thatutilize AI/ML models deployed in core hyper-automation system 240 (e.g.,as part of an AI center thereof). Such RPA robots may send input forexecution of the AI/ML model(s) and receive output therefrom via corehyper-automation system 240.

One or more of robots 230 may be listeners, as described above. Theselisteners may provide information to core hyper-automation system 240regarding what users are doing when they use their computing systems.This information may then be used by core hyper-automation system forprocess mining, task mining, task capture, etc.

An assistant/chatbot 250 may be provided on user computing systems toallow users to launch RPA local robots. The assistant may be located ina system tray, for example. Chatbots may have a user interface so userscan see text in the chatbot. Alternatively, chatbots may lack a userinterface and run in the background, listening using the computingsystem's microphone for user speech.

In some embodiments, data labeling may be performed by a user of thecomputing system on which a robot is executing or on another computingsystem that the robot provides information to. For instance, if a robotcalls an AI/ML model that performs CV on images for VM users, but theAI/ML model does not correctly identify a button on the screen, the usermay draw a rectangle around the misidentified or non-identifiedcomponent and potentially provide text with a correct identification.This information may be provided to core hyper-automation system 240 andthen used later for training a new version of the AI/ML model.

FIG. 3 is an architectural diagram illustrating a deployed RPA system300, according to an embodiment of the present invention. In someembodiments, RPA system 300 may be a part of RPA system 200 of FIG. 2and/or hyper-automation system 100 of FIG. 1 . Deployed RPA system 300may be a cloud-based system, an on-premises system, a desktop-basedsystem that offers enterprise level, user level, or device levelautomation solutions for automation of different computing processes,etc.

It should be noted that the client side, the server side, or both, mayinclude any desired number of computing systems without deviating fromthe scope of the invention. On the client side, a robot application 310includes executors 312, an agent 314, and a designer 316. However, insome embodiments, designer 316 may not be running on the same computingsystem as executors 312 and agent 314. Executors 312 are runningprocesses. Several business projects may run simultaneously, as shown inFIG. 3 . Agent 314 (e.g., a Windows® service) is the single point ofcontact for all executors 312 in this embodiment. All messages in thisembodiment are logged into conductor 340, which processes them furthervia database server 350, an AI/ML server 360, an indexer server 370, orany combination thereof. As discussed above with respect to FIG. 2 ,executors 312 may be robot components.

In some embodiments, a robot represents an association between a machinename and a username. The robot may manage multiple executors at the sametime. On computing systems that support multiple interactive sessionsrunning simultaneously (e.g., Windows® Server 2012), multiple robots maybe running at the same time, each in a separate Windows® session using aunique username. This is referred to as HD robots above.

Agent 314 is also responsible for sending the status of the robot (e.g.,periodically sending a “heartbeat” message indicating that the robot isstill functioning) and downloading the required version of the packageto be executed. The communication between agent 314 and conductor 340 isalways initiated by agent 314 in some embodiments. In the notificationscenario, agent 314 may open a WebSocket channel that is later used byconductor 330 to send commands to the robot (e.g., start, stop, etc.).

A listener 330 monitors and records data pertaining to user interactionswith an attended computing system and/or operations of an unattendedcomputing system on which listener 330 resides. Listener 330 may be anRPA robot, part of an operating system, a downloadable application forthe respective computing system, or any other software and/or hardwarewithout deviating from the scope of the invention. Indeed, in someembodiments, the logic of the listener is implemented partially orcompletely via physical hardware.

On the server side, a presentation layer (web application 342, Open DataProtocol (OData) Representative State Transfer (REST) ApplicationProgramming Interface (API) endpoints 344, and notification andmonitoring 346), a service layer (API implementation/business logic348), and a persistence layer (database server 350, AI/ML server 360,and indexer server 370) are included. Conductor 340 includes webapplication 342, OData REST API endpoints 344, notification andmonitoring 346, and API implementation/business logic 348. In someembodiments, most actions that a user performs in the interface ofconductor 340 (e.g., via browser 320) are performed by calling variousAPIs. Such actions may include, but are not limited to, starting jobs onrobots, adding/removing data in queues, scheduling jobs to rununattended, etc. without deviating from the scope of the invention. Webapplication 342 is the visual layer of the server platform. In thisembodiment, web application 342 uses Hypertext Markup Language (HTML)and JavaScript (JS). However, any desired markup languages, scriptlanguages, or any other formats may be used without deviating from thescope of the invention. The user interacts with web pages from webapplication 342 via browser 320 in this embodiment in order to performvarious actions to control conductor 340. For instance, the user maycreate robot groups, assign packages to the robots, analyze logs perrobot and/or per process, start and stop robots, etc.

In addition to web application 342, conductor 340 also includes servicelayer that exposes OData REST API endpoints 344. However, otherendpoints may be included without deviating from the scope of theinvention. The REST API is consumed by both web application 342 andagent 314. Agent 314 is the supervisor of one or more robots on theclient computer in this embodiment.

The REST API in this embodiment covers configuration, logging,monitoring, and queueing functionality. The configuration endpoints maybe used to define and configure application users, permissions, robots,assets, releases, and environments in some embodiments. Logging RESTendpoints may be used to log different information, such as errors,explicit messages sent by the robots, and other environment-specificinformation, for instance. Deployment REST endpoints may be used by therobots to query the package version that should be executed if the startjob command is used in conductor 340. Queueing REST endpoints may beresponsible for queues and queue item management, such as adding data toa queue, obtaining a transaction from the queue, setting the status of atransaction, etc.

Monitoring REST endpoints may monitor web application 342 and agent 314.Notification and monitoring API 346 may be REST endpoints that are usedfor registering agent 314, delivering configuration settings to agent314, and for sending/receiving notifications from the server and agent314. Notification and monitoring API 346 may also use WebSocketcommunication in some embodiments.

The APIs in the service layer may be accessed through configuration ofan appropriate API access path in some embodiments, e.g., based onwhether conductor 340 and an overall hyper-automation system have anon-premises deployment type or a cloud-based deployment type. APIs forconductor 340 may provide custom methods for querying stats aboutvarious entities registered in conductor 340. Each logical resource maybe an OData entity in some embodiments. In such an entity, componentssuch as the robot, process, queue, etc., may have properties,relationships, and operations. APIs of conductor 340 may be consumed byweb application 342 and/or agents 314 in two ways in some embodiments:by getting the API access information from conductor 340, or byregistering an external application to use the OAuth flow.

The persistence layer includes a trio of servers in thisembodiment—database server 350 (e.g., a SQL server), AI/ML server 360(e.g., a server providing AI/ML model serving services, such as AIcenter functionality) and indexer server 370. Database server 350 inthis embodiment stores the configurations of the robots, robot groups,associated processes, users, roles, schedules, etc. This information ismanaged through web application 342 in some embodiments. Database server350 may manage queues and queue items. In some embodiments, databaseserver 350 may store messages logged by the robots (in addition to or inlieu of indexer server 370). Database server 350 may also store processmining, task mining, and/or task capture-related data, received fromlistener 330 installed on the client side, for example. While no arrowis shown between listener 330 and database 350, it should be understoodthat listener 330 is able to communicate with database 350, and viceversa in some embodiments. This data may be stored in the form of PDDs,images, XAML files, etc. Listener 330 may be configured to interceptuser actions, processes, tasks, and performance metrics on therespective computing system on which listener 330 resides. For example,listener 330 may record user actions (e.g., clicks, typed characters,locations, applications, active elements, times, etc.) on its respectivecomputing system and then convert these into a suitable format to beprovided to and stored in database server 350.

AI/ML server 360 facilitates incorporation of AI/ML models intoautomations. Pre-built AI/ML models, model templates, and variousdeployment options may make such functionality accessible even to thosewho are not data scientists. Deployed automations (e.g., RPA robots) maycall AI/ML models from AI/ML server 360. Performance of the AI/ML modelsmay be monitored, and be trained and improved using human-validateddata. AI/ML server 360 may schedule and execute training jobs to trainnew versions of the AI/ML models.

AI/ML server 360 may store data pertaining to AI/ML models and MLpackages for configuring various ML skills for a user at developmenttime. An ML skill, as used herein, is a pre-built and trained ML modelfor a process, which may be used by an automation, for example. AI/MLserver 360 may also store data pertaining to document understandingtechnologies and frameworks, algorithms and software packages forvarious AI/ML capabilities including, but not limited to, intentanalysis, natural language processing (NLP), speech analysis, differenttypes of AI/ML models, etc.

Indexer server 370, which is optional in some embodiments, stores andindexes the information logged by the robots. In certain embodiments,indexer server 370 may be disabled through configuration settings. Insome embodiments, indexer server 370 uses ElasticSearch®, which is anopen source project full-text search engine. Messages logged by robots(e.g., using activities like log message or write line) may be sentthrough the logging REST endpoint(s) to indexer server 370, where theyare indexed for future utilization.

FIG. 4 is an architectural diagram illustrating the relationship 400between a designer 410, activities 420, 430, 440, 450, drivers 460, APIs470, and AI/ML models 480, according to an embodiment of the presentinvention. Per the above, a developer uses designer 410 to developworkflows that are executed by robots. The various types of activitiesmay be displayed to the developer in some embodiments. Designer 410 maybe local to the user's computing system or remote thereto (e.g.,accessed via VM or a local web browser interacting with a remote webserver). Workflows may include user-defined activities 420, API-drivenactivities 430, AI/ML activities 440, and/or and UI automationactivities 450. User-defined activities 420 and API-driven activities440 interact with applications via their APIs. User-defined activities420 and/or AI/ML activities 440 may call one or more AI/ML models 480 insome embodiments, which may be located locally to the computing systemon which the robot is operating and/or remotely thereto.

Some embodiments are able to identify non-textual visual components inan image, which is called CV herein. CV may be performed at least inpart by AI/ML model(s) 480. Some CV activities pertaining to suchcomponents may include, but are not limited to, extracting of text fromsegmented label data using OCR, fuzzy text matching, cropping ofsegmented label data using ML, comparison of extracted text in labeldata with ground truth data, etc. In some embodiments, there may behundreds or even thousands of activities that may be implemented inuser-defined activities 420. However, any number and/or type ofactivities may be used without deviating from the scope of theinvention.

UI automation activities 450 are a subset of special, lower-levelactivities that are written in lower-level code and facilitateinteractions with the screen. UI automation activities 450 facilitatethese interactions via drivers 460 that allow the robot to interact withthe desired software. For instance, drivers 460 may include operatingsystem (OS) drivers 462, browser drivers 464, VM drivers 466, enterpriseapplication drivers 468, etc. One or more of AI/ML models 480 may beused by UI automation activities 450 in order to perform interactionswith the computing system in some embodiments. In certain embodiments,AI/ML models 480 may augment drivers 460 or replace them completely.Indeed, in certain embodiments, drivers 460 are not included.

Drivers 460 may interact with the OS at a low level looking for hooks,monitoring for keys, etc. via OS drivers 462. Drivers 460 may facilitateintegration with Chrome®, IE®, Citrix®, SAP®, etc. For instance, the“click” activity performs the same role in these different applicationsvia drivers 460.

FIG. 5 is an architectural diagram illustrating a computing system 500configured to perform automatic data transfer between a source and atarget using semantic AI for RPA, according to an embodiment of thepresent invention. In some embodiments, computing system 500 may be oneor more of the computing systems depicted and/or described herein. Incertain embodiments, computing system 500 may be part of ahyper-automation system, such as that shown in FIGS. 1 and 2 . Computingsystem 500 includes a bus 505 or other communication mechanism forcommunicating information, and processor(s) 510 coupled to bus 505 forprocessing information. Processor(s) 510 may be any type of general orspecific purpose processor, including a Central Processing Unit (CPU),an Application Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Graphics Processing Unit (GPU), multiple instancesthereof, and/or any combination thereof. Processor(s) 510 may also havemultiple processing cores, and at least some of the cores may beconfigured to perform specific functions. Multi-parallel processing maybe used in some embodiments. In certain embodiments, at least one ofprocessor(s) 510 may be a neuromorphic circuit that includes processingelements that mimic biological neurons. In some embodiments,neuromorphic circuits may not require the typical components of a VonNeumann computing architecture.

Computing system 500 further includes a memory 515 for storinginformation and instructions to be executed by processor(s) 510. Memory515 can be comprised of any combination of random access memory (RAM),read-only memory (ROM), flash memory, cache, static storage such as amagnetic or optical disk, or any other types of non-transitorycomputer-readable media or combinations thereof. Non-transitorycomputer-readable media may be any available media that can be accessedby processor(s) 510 and may include volatile media, non-volatile media,or both. The media may also be removable, non-removable, or both.

Computing system 500 includes a communication device 520, such as atransceiver, to provide access to a communications network via awireless and/or wired connection. In some embodiments, communicationdevice 520 may include one or more antennas that are singular, arrayed,phased, switched, beamforming, beamsteering, a combination thereof, andor any other antenna configuration without deviating from the scope ofthe invention. Processor(s) 510 are further coupled via bus 505 to adisplay 525. Any suitable display device and haptic I/O may be usedwithout deviating from the scope of the invention.

A keyboard 530 and a cursor control device 535, such as a computermouse, a touchpad, etc., are further coupled to bus 505 to enable a userto interface with computing system 500. However, in certain embodiments,a physical keyboard and mouse may not be present, and the user mayinteract with the device solely through display 525 and/or a touchpad(not shown). Any type and combination of input devices may be used as amatter of design choice. In certain embodiments, no physical inputdevice and/or display is present. For instance, the user may interactwith computing system 500 remotely via another computing system incommunication therewith, or computing system 500 may operateautonomously.

Memory 515 stores software modules that provide functionality whenexecuted by processor(s) 510. The modules include an operating system540 for computing system 500. The modules further include an automaticdata transfer module 545 that is configured to perform all or part ofthe processes described herein or derivatives thereof. Computing system500 may include one or more additional functional modules 550 thatinclude additional functionality.

One skilled in the art will appreciate that a “computing system” couldbe embodied as a server, an embedded computing system, a personalcomputer, a console, a personal digital assistant (PDA), a cell phone, atablet computing device, a quantum computing system, or any othersuitable computing device, or combination of devices without deviatingfrom the scope of the invention. Presenting the above-describedfunctions as being performed by a “system” is not intended to limit thescope of the present invention in any way, but is intended to provideone example of the many embodiments of the present invention. Indeed,methods, systems, and apparatuses disclosed herein may be implemented inlocalized and distributed forms consistent with computing technology,including cloud computing systems. The computing system could be part ofor otherwise accessible by a local area network (LAN), a mobilecommunications network, a satellite communications network, theInternet, a public or private cloud, a hybrid cloud, a server farm, anycombination thereof, etc. Any localized or distributed architecture maybe used without deviating from the scope of the invention.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, include one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may include disparate instructions stored in differentlocations that, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, RAM, tape, and/or any other suchnon-transitory computer-readable medium used to store data withoutdeviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

Various types of AI/ML models may be trained and deployed withoutdeviating from the scope of the invention. For instance, FIG. 6Aillustrates an example of a neural network 600 that has been trained torecognize graphical elements in an image, according to an embodiment ofthe present invention. Here, neural network 600 receives pixels of ascreenshot image of a 1920×1080 screen as input for input “neurons” 1 toI of the input layer. In this case, I is 2,073,600, which is the totalnumber of pixels in the screenshot image.

Neural network 600 also includes a number of hidden layers. Both DLNNsand shallow learning neural networks (SLNNs) usually have multiplelayers, although SLNNs may only have one or two layers in some cases,and normally fewer than DLNNs. Typically, the neural networkarchitecture includes an input layer, multiple intermediate layers, andan output layer, as is the case in neural network 600.

A DLNN often has many layers (e.g., 10, 50, 200, etc.) and subsequentlayers typically reuse features from previous layers to compute morecomplex, general functions. A SLNN, on the other hand, tends to haveonly a few layers and train relatively quickly since expert features arecreated from raw data samples in advance. However, feature extraction islaborious. DLNNs, on the other hand, usually do not require expertfeatures, but tend to take longer to train and have more layers.

For both approaches, the layers are trained simultaneously on thetraining set, normally checking for overfitting on an isolatedcross-validation set. Both techniques can yield excellent results, andthere is considerable enthusiasm for both approaches. The optimal size,shape, and quantity of individual layers varies depending on the problemthat is addressed by the respective neural network.

Returning to FIG. 6A, pixels provided as the input layer are fed asinputs to the J neurons of hidden layer 1. While all pixels are fed toeach neuron in this example, various architectures are possible that maybe used individually or in combination including, but not limited to,feed forward networks, radial basis networks, deep feed forwardnetworks, deep convolutional inverse graphics networks, convolutionalneural networks, recurrent neural networks, artificial neural networks,long/short term memory networks, gated recurrent unit networks,generative adversarial networks, liquid state machines, auto encoders,variational auto encoders, denoising auto encoders, sparse autoencoders, extreme learning machines, echo state networks, Markov chains,Hopfield networks, Boltzmann machines, restricted Boltzmann machines,deep residual networks, Kohonen networks, deep belief networks, deepconvolutional networks, support vector machines, neural Turing machines,or any other suitable type or combination of neural networks withoutdeviating from the scope of the invention.

Hidden layer 2 receives inputs from hidden layer 1, hidden layer 3receives inputs from hidden layer 2, and so on for all hidden layersuntil the last hidden layer provides its outputs as inputs for theoutput layer. It should be noted that numbers of neurons I, J, K, and Lare not necessarily equal, and thus, any desired number of layers may beused for a given layer of neural network 600 without deviating from thescope of the invention. Indeed, in certain embodiments, the types ofneurons in a given layer may not all be the same.

Neural network 600 is trained to assign a confidence score to graphicalelements believed to have been found in the image. In order to reducematches with unacceptably low likelihoods, only those results with aconfidence score that meets or exceeds a confidence threshold may beprovided in some embodiments. For instance, if the confidence thresholdis 80%, outputs with confidence scores exceeding this amount may be usedand the rest may be ignored. In this case, the output layer indicatesthat two text fields, a text label, and a submit button were found.Neural network 600 may provide the locations, dimensions, images, and/orconfidence scores for these elements without deviating from the scope ofthe invention, which can be used subsequently by an RPA robot or anotherprocess that uses this output for a given purpose.

It should be noted that neural networks are probabilistic constructsthat typically have a confidence score. This may be a score learned bythe AI/ML model based on how often a similar input was correctlyidentified during training. For instance, text fields often have arectangular shape and a white background. The neural network may learnto identify graphical elements with these characteristics with a highconfidence. Some common types of confidence scores include a decimalnumber between 0 and 1 (which can be interpreted as a percentage ofconfidence), a number between negative co and positive co, or a set ofexpressions (e.g., “low,” “medium,” and “high”). Various post-processingcalibration techniques may also be employed in an attempt to obtain amore accurate confidence score, such as temperature scaling, batchnormalization, weight decay, negative log likelihood (NLL), etc.

“Neurons” in a neural network are mathematical functions that that aretypically based on the functioning of a biological neuron. Neuronsreceive weighted input and have a summation and an activation functionthat governs whether they pass output to the next layer. This activationfunction may be a nonlinear thresholded activity function where nothinghappens if the value is below a threshold, but then the functionlinearly responds above the threshold (i.e., a rectified linear unit(ReLU) nonlinearity). Summation functions and ReLU functions are used indeep learning since real neurons can have approximately similar activityfunctions. Via linear transforms, information can be subtracted, added,etc. In essence, neurons act as gating functions that pass output to thenext layer as governed by their underlying mathematical function. Insome embodiments, different functions may be used for at least someneurons.

An example of a neuron 610 is shown in FIG. 6B. Inputs x₁, x₂, . . . ,x_(n) from a preceding layer are assigned respective weights w₁, w₂, . .. , w_(n). Thus, the collective input from preceding neuron 1 is w₁x₁.These weighted inputs are used for the neuron's summation functionmodified by a bias, such as:

$\begin{matrix}{{\sum\limits_{i = 1}^{m}\left( {w_{i}x_{i}} \right)} + {bias}} & (1)\end{matrix}$

This summation is compared against an activation function ƒ(x) todetermine whether the neuron “fires”. For instance, ƒ(x) may be givenby:

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{{{1{if}{\sum{wx}}} + {bias}} \geq 0} \\{{{0{if}{\sum{wx}}} + {bias}} < 0}\end{matrix} \right.} & (2)\end{matrix}$

The output y of neuron 710 may thus be given by:

$\begin{matrix}{y = {{{f(x)}{\sum\limits_{i = 1}^{m}\left( {w_{i}x_{i}} \right)}} + {bias}}} & (3)\end{matrix}$

In this case, neuron 610 is a single-layer perceptron. However, anysuitable neuron type or combination of neuron types may be used withoutdeviating from the scope of the invention. It should also be noted thatthe ranges of values of the weights and/or the output value(s) of theactivation function may differ in some embodiments without deviatingfrom the scope of the invention.

The goal, or “reward function” is often employed, such as for this casethe successful identification of graphical elements in the image. Areward function explores intermediate transitions and steps with bothshort-term and long-term rewards to guide the search of a state spaceand attempt to achieve a goal (e.g., successful identification ofgraphical elements, successful identification of a next sequence ofactivities for an RPA workflow, etc.).

During training, various labeled data (in this case, images) are fedthrough neural network 600. Successful identifications strengthenweights for inputs to neurons, whereas unsuccessful identificationsweaken them. A cost function, such as mean square error (MSE) orgradient descent may be used to punish predictions that are slightlywrong much less than predictions that are very wrong. If the performanceof the AI/ML model is not improving after a certain number of trainingiterations, a data scientist may modify the reward function, provideindications of where non-identified graphical elements are, providecorrections of misidentified graphical elements, etc.

Backpropagation is a technique for optimizing synaptic weights in afeedforward neural network. Backpropagation may be used to “pop thehood” on the hidden layers of the neural network to see how much of theloss every node is responsible for, and subsequently updating theweights in such a way that minimizes the loss by giving the nodes withhigher error rates lower weights, and vice versa. In other words,backpropagation allows data scientists to repeatedly adjust the weightsso as to minimize the difference between actual output and desiredoutput.

The backpropagation algorithm is mathematically founded in optimizationtheory. In supervised learning, training data with a known output ispassed through the neural network and error is computed with a costfunction from known target output, which gives the error forbackpropagation. Error is computed at the output, and this error istransformed into corrections for network weights that will minimize theerror.

In the case of supervised learning, an example of backpropagation isprovided below. A column vector input x is processed through a series ofN nonlinear activity functions ƒ_(i) between each layer i=1, . . . , Nof the network, with the output at a given layer first multiplied by asynaptic matrix W_(i), and with a bias vector b_(i) added. The networkoutput o, given by

o=ƒ _(N)(W _(N)ƒ_(N-1)(W _(N-1)ƒ_(N-2)( . . . ƒ₁(W ₁ x+b ₁) . . . )+b_(N-1))+b _(N))  (4)

In some embodiments, o is compared with a target output t, resulting inan error

${E = {\frac{1}{21}{{o - t}}^{2}}},$

which is desired to be minimized.

Optimization in the form of a gradient descent procedure may be used tominimize the error by modifying the synaptic weights W_(i) for eachlayer. The gradient descent procedure requires the computation of theoutput o given an input x corresponding to a known target output t, andproducing an error o−t. This global error is then propagated backwardsgiving local errors for weight updates with computations similar to, butnot exactly the same as, those used for forward propagation. Inparticular, the backpropagation step typically requires an activityfunction of the form p_(j)(n_(j))=ƒ_(j)′(n_(j)), where n_(j) is thenetwork activity at layer j (i.e., n_(j)=W_(j)o_(j-1)+b_(j)) whereo_(j)=ƒ_(j)(n_(j)) and the apostrophe ′ denotes the derivative of theactivity function ƒ.

The weight updates may be computed via the formulae:

$\begin{matrix}{d_{j} = \left\{ \begin{matrix}{{\left( {o - t} \right) \circ {p_{j}\left( n_{j} \right)}},} & {j = N} \\{{W_{j + 1}^{T}{d_{j + 1} \circ {p_{j}\left( n_{j} \right)}}},} & {j < N}\end{matrix} \right.} & (5)\end{matrix}$ $\begin{matrix}{\frac{\partial E}{\partial W_{j + 1}} = {d_{j + 1}\left( o_{j} \right)}^{T}} & (6)\end{matrix}$ $\begin{matrix}{\frac{\partial E}{\partial b_{j + 1}} = d_{j + 1}} & (7)\end{matrix}$ $\begin{matrix}{W_{j}^{new} = {W_{j}^{old} - {\eta\frac{\partial E}{\partial W_{j}}}}} & (8)\end{matrix}$

where ∘ denotes a Hadamard product (i.e., the element-wise product oftwo vectors), ^(T) denotes the matrix transpose, and o_(j) denotesƒ_(j)(W_(j)o_(j-1)+b_(j)), with o₀=x. Here, the learning rate η ischosen with respect to machine learning considerations. Below, η isrelated to the neural Hebbian learning mechanism used in the neuralimplementation. Note that the synapses W and b can be combined into onelarge synaptic matrix, where it is assumed that the input vector hasappended ones, and extra columns representing the b synapses aresubsumed to W.

The AI/ML model may be trained over multiple epochs until it reaches agood level of accuracy (e.g., 97% or better using an F2 or F4 thresholdfor detection and approximately 2,000 epochs). This accuracy level maybe determined in some embodiments using an F1 score, an F2 score, an F4score, or any other suitable technique without deviating from the scopeof the invention. Once trained on the training data, the AI/ML model maybe tested on a set of evaluation data that the AI/ML model has notencountered before. This helps to ensure that the AI/ML model is not“over fit” such that it identifies graphical elements in the trainingdata well, but does not generalize well to other images.

In some embodiments, it may not be known what accuracy level is possiblefor the AI/ML model to achieve. Accordingly, if the accuracy of theAI/ML model is starting to drop when analyzing the evaluation data(i.e., the model is performing well on the training data, but isstarting to perform less well on the evaluation data), the AI/ML modelmay go through more epochs of training on the training data (and/or newtraining data). In some embodiments, the AI/ML model is only deployed ifthe accuracy reaches a certain level or if the accuracy of the trainedAI/ML model is superior to an existing deployed AI/ML model.

In certain embodiments, a collection of trained AI/ML models may be usedto accomplish a task, such as employing an AI/ML model for each type ofgraphical element of interest, employing an AI/ML model to perform OCR,deploying yet another AI/ML model to recognize proximity relationshipsbetween graphical elements, employing still another AI/ML model togenerate an RPA workflow based on the outputs from the other AI/MLmodels, etc. This may collectively allow the AI/ML models to enablesemantic automation, for instance.

Some embodiments may use transformer networks such asSentenceTransformers™, which is a Python™ framework for state-of-the-artsentence, text, and image embeddings. Such transformer networks learnassociations of words and phrases that have both high scores and lowscores. This trains the AI/ML model to determine what is close to theinput and what is not, respectively. Rather than just using pairs ofwords/phrases, transformer networks may use the field length and fieldtype, as well.

FIG. 7 is a flowchart illustrating a process 700 for training AI/MLmodel(s), according to an embodiment of the present invention. Theprocess begins with providing training data, for instance, labeled dataas shown in FIG. 7 , such as labeled screens (e.g., with graphicalelements and text identified), words and phrases, a “thesaurus” ofsemantic associations between words and phrases such that similar wordsand phrases for a given word or phrase can be identified, etc. at 710.The nature of the training data that is provided will depend on theobjective that the AI/ML model is intended to achieve. The AI/ML modelis then trained over multiple epochs at 720 and results are reviewed at730.

If the AI/ML model fails to meet a desired confidence threshold at 740,the training data is supplemented and/or the reward function is modifiedto help the AI/ML model achieve its objectives better at 750 and theprocess returns to step 720. If the AI/ML model meets the confidencethreshold at 740, the AI/ML model is tested on evaluation data at 760 toensure that the AI/ML model generalizes well and that the AI/ML model isnot over fit with respect to the training data. The evaluation data mayinclude screens, source data, etc. that the AI/ML model has notprocessed before. If the confidence threshold is met at 770 for theevaluation data, the AI/ML model is deployed at 780. If not, the processreturns to step 750 and the AI/ML model is trained further.

Some embodiments bring semantic automation into automation platforms forcreating fully automated workflows with less or minimal interactioninput from the developer. Using semantic mapping, the UI fields from adata source/source screen are mapped to UI fields on the target screensemantically using one or more AI/ML models, and fully automatedworkflows can be created from this semantic mapping without interventionby the developer. In a current prototype, the mapping can be achievedfor up to 80% of the UI fields, and with developer assistance, theremaining ˜20% can be mapped. The AI/ML model(s) can be retrained tolearn to match the UI fields more accurately over time, with theexpectation that the mapping will approach 100% accuracy in the future.

In some embodiments, the RPA designer application includes a semanticmatching feature that allows RPA developers to perform a match betweentwo screens or between data (e.g., customer data) and a screen. Uponselection of semantic AI functionality, the RPA designer application maydisplay a matching interface, such as matching interface 800 of FIGS.8A-D. While this is a general example, many use cases exist for thesemantic AI provided by embodiments of the present invention, such asmapping an invoice to SAP®, automatically inputting data from an excelspreadsheet into a CRM application, mapping XAML from an RPA workflow toanother RPA workflow, etc. Also, while this example consists of textfields, other graphical elements, such as buttons, text areas, datawithout a visual display, etc. may be mapped without deviating from thescope of the invention.

Matching interface 800 includes a mapping options pane 810 and a mappingpane 820. When the developer (including citizen developers with littleor no programming experience) chooses select source(s) 822 or selecttarget 824, the user can select the source of the data and the target towhich the source data will be copied, respectively. It is possible thatmultiple sources may be used, and the data therefrom can be stored(e.g., collected in a single data storage object) for matching with thetarget. This source information is then used for filling in the target.Select source(s) button 822 allows the user to designate multiplesources in this embodiment. These sources may be learned by capturingthe user's source selection and future copy-and-paste operations forthat application may employ these sources automatically, or offer thetechnique to the user as an option.

In some embodiments, the user may select the system clipboard as asource via select source(s) button 822. When a user adds information tothe system clipboard (e.g., when a user presses CTRL+C and copiesselected content in Microsoft Windows®), this information may includedata that the user would like to enter into the target in someembodiments. This information can be obtained by a suitable operatingsystem API, such as via System.Windows.Forms.dll usingClipboard.GetText( ) for Windows®. Semantic mapping can then beperformed from the clipboard to the target. If the clipboard content iscopied text, such as a sentence, paragraph, etc., NLP may be applied tothis information to obtain content for sematic matching with the target.

In some embodiments, when the user chooses select source 822 or selecttarget 824, a type selection interface 860 is shown. In FIG. 8B, afterthe user chooses select source 822, type selection interface 860provides a dropdown 862 listing various types that are recognized andsupported by the RPA designer application. The user can then clickconfirm button 864 to confirm the type if one of the types pertains tothe source or target. Based on this selection, the RPA designerapplication may be able to determine with more certainty what the labelsand fields are in the source and/or the target.

Once selected, if they have visible interfaces, source screen 840 andtarget screen 850 are shown. See FIG. 8C. However, in some embodiments,this step may be skipped, or potentially, no display for these screensis provided to the user.

When the user presses “Semantic CP” button 812 to perform a semanticcopy-and-paste, the semantic matching AI/ML model is called to matchsource labels and values with target labels and fields. The values formatched fields are then automatically copied from the source to thetarget. See FIG. 8D. However, in this case, “Invoice #” in the sourcewas not mapped to “Inv. Num.” in the target and has a confidence scoreof 0. Accordingly, the field 842 in the source that was not matched ishighlighted 842. The developer can then manually map the labels/fieldsin the source and target that match, and this mapping can be saved forretraining of the semantic matching AI/ML model. For instance, thesource and target screens may be saved, along with bounding boxinformation (e.g., coordinates) and the coordinates and text of labelsassociated with the matched fields in source screen 840 and targetscreen 850.

Relationships between labels in the source screen and target screen maybe used to determine what a given text field is meant to represent,although the text fields may be similar to or the same as one another.This may be accomplished by assigning one or more anchors to a giventext field. For instance, because the field “City” appears directly tothe left of it associated text field in target screen 850 and no othertext field includes this label, the designer application and/or semanticmatching AI/ML model(s) may determine that these fields are linked, andassign the City label as an anchor for the target text field. If thelabel does not uniquely identify the text field, one or more othergraphical elements may be assigned as anchors, and their geometricrelationships may be used to uniquely identify the given target element.See, for example, U.S. Pat. Nos. 10,936,351 and 11,200,073.

After the source screen or source data and the target screen have beenmapped, the user can click Create button 834 to automatically generateone or more activities in the RPA workflow that implement the desiredmapping. This causes the RPA workflow activities to be automaticallycreated. In some embodiments, the RPA workflow is immediately executedto perform the mapping task desired by the user after creation.

To automatically create the RPA workflow, the designer application maymake use of a UI object repository. See, for example, U.S. PatentApplication Publication No. 2022/0012024. A UI object repository (e.g.,the UiPath Object Repository™) is a collection of UI object libraries,which are themselves collections of UI descriptors (e.g., for a certainversion of an application and one or more screens thereof). Unifiedtarget controls for similar graphical elements can be obtained from theUI object repository, which instruct the RPA robot how to interact witha given graphical element.

Such an example is shown in FIG. 9 , which illustrates an RPA designerapplication 900 with automatically generated activities in an RPAworkflow 910, according to an embodiment of the present invention. Thesemantic matching AI/ML model(s) have been trained to recognizeassociations between the source screen or source data and the targetscreen, per the above. In the case of the example of FIGS. 8A-C and 9,the semantic matching AI/ML model(s) are able to determine that datafrom fields in the source screen or source data should be copied intothe matching fields in the target screen. Accordingly, RPA designerapplication 900 knows to obtain UI descriptors for the target elementsfrom the UI object repository, add activities to RPA workflow 910 thatclick on the target screen, click on each target field, and enter thetext from the source screen or data source into the respective matchingfields in the target screen using these UI descriptors. RPA designerapplication 900 automatically generates one or more activities in RPAworkflow 910 that implement this functionality. In some embodiments, thedeveloper may not be permitted to modify these activities. However, incertain embodiments, the developer may eb able to modify configurationsfor the activities, have full permissions for editing the activities,etc. In some embodiments, the RPA designer application automaticallygenerates an RPA robot implementing the RPA workflow and executes theRPA robot so information from the source screen or source data isautomatically copied into the target screen without further directionfrom the developer.

Some embodiments provide a semantic copy and paste feature that allowsusers at runtime without substantial programming experience to performsemantic automation. FIG. 10A illustrates a semantic copy and pasteinterface 1000, according to an embodiment of the present invention. Insome embodiments, semantic copy and paste interface 1000 is part of anautomation executed by an RPA robot. Semantic copy and paste interface1000 includes a copy and paste button 1010 and a close button 1030.Using semantic copy and paste interface 1000, a user can or perform acopy and paste from a source to a target. The source and/or target maybe files, application interfaces, or any other suitable vehicle that iscapable of storing data without deviating from the scope of theinvention, and the type of the source and the target may differ from oneanother.

Upon clicking copy and paste button 1010, the application (e.g., an RPArobot executing an automation) asks the user to indicate an applicationor file that he or she wants to copy data from as a source via a sourceselection interface 1012. See FIG. 10B. When the user indicate sourcebutton 1014 of source selection interface 1012, indicate on screenfunctionality is enabled in some embodiments (e.g., the same as orsimilar to that provided by UiPath Studio™). The user can then selectinvoice 1016 as the source.

After indicating the source (i.e., invoice 1013 in this example), thesemantic automation logic (i.e., the semantic matching AI/ML models(s))can predict the type of the source using a classification algorithm, anddata extraction interface 1012 displays its prediction of the type ofthe source in dropdown menu 1016. See FIG. 10C. However, in someembodiments, the user may not be prompted to confirm and/or select thesource type. The user can confirm the prediction using confirm button1017 or select another type from dropdown menu 1016. See FIG. 10D.

After the source is selected, the application asks the user to indicatethe application or file that he or she wants to copy data to. See FIG.10E. However, in some embodiments, the target may be selected firstand/or the order of the selection of the source and the target does notmatter. When the user clicks indicate target button 1024 of targetselection interface 1022, indicate on screen functionality is enabled inthis embodiment. The user can then select a web invoice processing page1023 as the target.

After indicating web invoice processing page 1023 as the target, thesemantic automation logic can predict the type of the target using theclassification algorithm, and target selection interface 1022 displaysits prediction of the type of the target in dropdown menu 1026. See FIG.10F. The user can confirm the prediction using confirm button 1027 orselect another type from dropdown menu 1026. See FIG. 10G. Afterconfirmation by the user, the application automatically populates webbrowser 1023 using the extracted data. See FIG. 10H.

FIG. 12 is an architectural diagram illustrating an architecture 1100 ofthe AI/ML models for performing semantic AI, according to an embodimentof the present invention. A CV model 1110 performs computer visionfunctionality to identify graphical elements in a screen and an OCRmodel 1120 performs text detection and recognition for the screen(s). Inembodiments where both a source screen and a target screen are used, CVmodel 1110 and OCR model 1120 perform CV and OCR functionality on bothscreens.

CV model 1110 and OCR model 1120 then provide types, locations, sizes,text, etc. of the detected graphical elements and text in the targetscreen or both the target and source screen to a label matching model1130 that matches labels from OCR model 1120 with graphical elementsfrom CV model 1110. Matching labels and the associated graphicalelements from the screen(s) are then passed to an input data matchingmodel 1140, which matches input data from a source with labels in thetarget. The matches and the respective confidences are then provided asoutput from input data matching model 1140. In some embodiments,multiple AI/ML models may be used for input data matching that performmatching in different ways (e.g., they have different neural networkarchitectures, employ different strategies, have been trained ondifferent training data, etc.).

In some embodiments, the AI/ML model(s) may learn that fields with thesame labels may have different context. For instance, both a billinginformation and a shipping information section of a screen may have an“Address” label, but the AI/ML model may learn that the pattern of theelements near one differs from those near the other. These sections ofthe screen may then be used as anchors in a multi-anchor technique wherethe text field is the target and the “Address” label and the sectionwith the recognized pattern are the anchors. See, for example, U.S. Pat.Nos. 10,936,351 and 11,200,073.

FIG. 12 is a flowchart illustrating a process 1200 for performingautomatic data transfer between a source and a target using semantic AIfor RPA at design time, according to an embodiment of the presentinvention. The process begins with receiving a selection of a source at1205 and receiving a selection of a target at 1210. One or more AI/MLmodels that have been trained to perform semantic matching and datatransfer between the source and the target are then called at 1215. Insome embodiments, the one or more AI/ML models are trained by providingwords and phrases with semantic associations between the words andphrases such that similar words and phrases for a given word or phrasecan be identified and providing contextual labels. In some embodiments,the one or more AI/ML models include a CV model, an OCR model, a labelmatching model, and an input data matching model, where the labelmatching model matches labels detected by the OCR model with fieldsdetected by the CV model and the input data model receives the matchinglabels from the label matching model and semantically matches the dataelements from the source or from the fields associated with the labelsfrom the source with the fields associated with the semantically matchedlabels on the target.

Indications of values or locations associated with semantically matchedlabels in the target (e.g., locations, coordinates, type, etc.) andrespective confidence scores from the one or more AI/ML models arereceived at 1220. The values or locations associated with thesemantically matched labels, individual confidence scores, and a globalconfidence score are displayed on the source and/or target in a matchinginterface at 1225. For instance, the target screen may be shown andmatching elements may be highlighted or otherwise made obvious to thedeveloper. In some embodiments, connections are drawing between matchingfields in the source screen or source data and the target screen. Incertain embodiments, elements in the source screen or source data forwhich no match was found are highlighted or otherwise indicated to thedeveloper.

Correction(s) to values or locations in the target screen identified bythe one or more AI/ML models as having an associated semanticallymatching label, an indication of a new element in the target screen thatwas not semantically matched to a label in the source by the one or moreAI/ML models, or both, are received at 1230. Information pertaining tothe corrected and/or newly labeled values or locations in the target andthe associated label are collected and stored either directly (i.e.,stored directly in computing system memory) or indirectly (i.e., sent toan external system for storage) at 1235. Steps 1230 and 1235 areperformed if such corrections are provided by the developer.

One or more activities in an RPA workflow that copy semantically matcheddata from the source to the target are automatically generated at 1240.In some embodiments, the automatic generation of the one or moreactivities includes determining the start of a copy-and-paste task(e.g., a copy operation), determining the end of the copy-and-paste task(e.g., a paste operation), determine operations in between (if any), andgenerate associated activities for these operations. An RPA automationimplementing the one or more generated activities in the RPA workflow isgenerated and deployed at 1245.

At runtime, an RPA robot runs the automation to accesses UI descriptorsfor graphical elements it is trying to identify to perform theautomation in accordance with the RPA workflow from a UI objectrepository and attempts to identify graphical elements in the targetusing these UI descriptors at 1250. If all target graphical elements canbe identified at 1255, the information is copied from the source to thetarget at 1260. However, if all graphical elements cannot be found at1255, the RPA robot calls the AI/ML model(s) to attempt to identify themissing graphical element(s) and updates the UI descriptors for theserespective graphical elements at 1265. For instance, the RPA robot mayuse the descriptor information provided by the AI/ML model(s) to updatethe respective UI descriptors for the missing elements in the UI objectrepository so other RPA robots will not encounter the same issue in thefuture. In this sense, the system is self-healing.

FIG. 13 is a flowchart illustrating a process 1300 for performingautomatic data transfer between a source and a target using semantic AIfor RPA at runtime, according to an embodiment of the present invention.The process begins with providing a semantic copy and paste interface at1305. A source is identified at 1310. The type of the source ispredicted using a classification algorithm at 1315. In some embodiments,the semantic copy and paste application waits to receive confirmation ofthe prediction or a change to the prediction by a user at 1320.

An indication of the target application is received at 1325. The type ofthe target is predicted using a classification algorithm at 1330. Insome embodiments, the semantic copy and paste application waits toreceive confirmation of the prediction or a change to the prediction bya user at 1335.

In some embodiments, the user is prompted before each data entry in thecopy and paste functionality at 1340. For instance, before entering agiven data item (e.g., a line of data, an individual graphical element,etc.), the user may see the data to be input appear in the target. Theuser may then preview and approve the entry or decline. Data from thesource is then entered into the target at 1345.

In some embodiments, persistence of data across multiple screens and/orscreen versions may be desired. For instance, an account creationprocess for an application may include multiple screens with variousfields, some of which are potentially repeated. Additionally oralternatively, some labels/fields for a screen may not appear until someaction is taken (e.g., selecting a radio button, clicking a checkbox,completing earlier entries, etc.).

Metadata from a user's clipboard and/or previous screen entries may bestored in a persistent data structure that persists and grows as eachscreen of information is completed. Values for labels (e.g., numbers,strings of alphanumeric values or characters, currency values, images insome embodiments, etc.) may automatically be added to associated fieldsif a confidence threshold is exceeded (e.g., 70%, 90%, 99%, etc.),depending on the accuracy from training. However, if this confidencethreshold (i.e., an autocompletion threshold) is not met for a label,the user may be presented with options that are above a second, lowersuggestion threshold (e.g., 40%, 60%, 75%, etc.). These options may beprovided when the user clicks on the field, hovers the mouse over thefield, etc. The user can then select a value from these options or add adifferent value. This value is then added to the persistent datastructure for that label.

As information is added to the screens and the persistent data structuregrows, the semantic associations and suggestions may become moreaccurate. Also, classification of the document, application, webpage(s),etc. that is being completed becomes more accurate or potentiallypossible if not previously possible before data entry. In this manner,the semantic features may progressively improve. In certain embodiments,the persistent data structure(s) from previous data entry sessions maybe used to make the semantic logic more accurate and/or to providetemplates for future completions. The data structure may includeassociations of labels to values, and potentially associations oflabel/value pairs to screen(s), which may assist in templatefunctionality.

FIG. 14 is a flowchart illustrating a process 1400 for performingautomatic persistent, multi-screen data transfer between a source and atarget using semantic AI for RPA at design time, according to anembodiment of the present invention. The process begins with receiving aselection of a source at 1405 and receiving a selection of a target at1410. One or more AI/ML models that have been trained to performsemantic matching and data transfer between the source and the targetare then called at 1415. In some embodiments, the one or more AI/MLmodels are trained by providing words and phrases with semanticassociations between the words and phrases such that similar words andphrases for a given word or phrase can be identified and providingcontextual labels. In some embodiments, the one or more AI/ML modelsinclude a CV model, an OCR model, a label matching model, and an inputdata matching model, where the label matching model matches labelsdetected by the OCR model with fields detected by the CV model and theinput data model receives the matching labels from the label matchingmodel and semantically matches the data elements from the source or fromthe fields associated with the labels from the source with the fieldsassociated with the semantically matched labels on the target.

Indications of values or locations associated with semantically matchedlabels in the target (e.g., locations, coordinates, type, etc.) andrespective confidence scores from the one or more AI/ML models arereceived at 1420. The values or locations associated with thesemantically matched labels, individual confidence scores, and a globalconfidence score are displayed on the source and/or target in a matchinginterface at 1425. For instance, the target screen may be shown andmatching elements may be highlighted or otherwise made obvious to thedeveloper. In some embodiments, connections are drawing between matchingfields in the source screen or source data and the target screen. Incertain embodiments, elements in the source screen or source data forwhich no match was found are highlighted or otherwise indicated to thedeveloper.

Correction(s) to values or locations in the target screen identified bythe one or more AI/ML models as having an associated semanticallymatching label, an indication of a new element in the target screen thatwas not semantically matched to a label in the source by the one or moreAI/ML models, or both, are received at 1430. Information pertaining tothe corrected and/or newly labeled values or locations in the target andthe associated label are collected and stored either directly (i.e.,stored directly in computing system memory) or indirectly (i.e., sent toan external system for storage) in a persistent data structure thatincludes label/field associations at 1435. If there is a new screenand/or a modification to a current screen such that fields that wereeither not visible or completion of which was blocked by the application(e.g., grayed out and not fillable by a user) at 1440, the processreturns to step 1420 for that screen.

One or more activities in an RPA workflow that copy semantically matcheddata from the source to the target are automatically generated at 1445.In some embodiments, the automatic generation of the one or moreactivities includes determining the start of a copy-and-paste task(e.g., a copy operation), determining the end of the copy-and-paste task(e.g., a paste operation), determine operations in between (if any), andgenerate associated activities for these operations. An RPA automationimplementing the one or more generated activities in the RPA workflow isgenerated and deployed at 1450.

At runtime, an RPA robot runs the automation to accesses UI descriptorsfor graphical elements it is trying to identify to perform theautomation in accordance with the RPA workflow from a UI objectrepository and attempts to identify graphical elements in the targetusing these UI descriptors at 1455. If all target graphical elements canbe identified at 1460, the information is copied from the source to thetarget at 1465. However, if all graphical elements cannot be found at1460, the RPA robot calls the AI/ML model(s) to attempt to identify themissing graphical element(s) and updates the UI descriptors for theserespective graphical elements at 1470. For instance, the RPA robot mayuse the descriptor information provided by the AI/ML model(s) to updatethe respective UI descriptors for the missing elements in the UI objectrepository so other RPA robots will not encounter the same issue in thefuture. In this sense, the system is self-healing.

FIG. 15 is a flowchart illustrating a process 1500 for performingpersistent, multi-screen automatic data transfer between a source and atarget using semantic AI for RPA at runtime, according to an embodimentof the present invention. The process begins with providing a semanticcopy and paste interface at 1505. A source is identified at 1510. Thetype of the source is predicted using a classification algorithm at1515. In some embodiments, the semantic copy and paste application waitsto receive confirmation of the prediction or a change to the predictionby a user at 1520.

An indication of the target application is received at 1525. The type ofthe target is predicted using a classification algorithm at 1530. Insome embodiments, the semantic copy and paste application waits toreceive confirmation of the prediction or a change to the prediction bya user at 1535.

In some embodiments, the user is prompted before each data entry in thecopy and paste functionality at 1540. For instance, before entering agiven data item (e.g., a line of data, an individual graphical element,etc.), the user may see the data to be input appear in the target. Theuser may then preview and approve the entry or decline. Data from thesource is then entered into the target at 1545.

Suggestions may be presented to the user at 1550 for labels/fields thatdo not meet a first autocompletion confidence threshold but are above asecond, lower suggestion confidence threshold. These suggestions may bebased at least in part on data in a persistent data structure. Valuesfor labels/fields that have been entered are stored in the persistentdata structure at 1555. If there is a new screen and/or a modificationto a current screen such that fields that were either not visible orcompletion of which was blocked by the application (e.g., grayed out andnot fillable by a user) at 1560, the process returns to step 1545 forthat screen.

The process steps performed in FIGS. 12-15 may be performed by acomputer program, encoding instructions for the processor(s) to performat least part of the process(es) described in FIGS. 12-15 , inaccordance with embodiments of the present invention. The computerprogram may be embodied on a non-transitory computer-readable medium.The computer-readable medium may be, but is not limited to, a hard diskdrive, a flash device, RAM, a tape, and/or any other such medium orcombination of media used to store data. The computer program mayinclude encoded instructions for controlling processor(s) of a computingsystem (e.g., processor(s) 510 of computing system 500 of FIG. 5 ) toimplement all or part of the process steps described in FIGS. 12-15 ,which may also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or ahybrid implementation. The computer program can be composed of modulesthat are in operative communication with one another, and which aredesigned to pass information or instructions to display. The computerprogram can be configured to operate on a general purpose computer, anASIC, or any other suitable device.

It will be readily understood that the components of various embodimentsof the present invention, as generally described and illustrated in thefigures herein, may be arranged and designed in a wide variety ofdifferent configurations. Thus, the detailed description of theembodiments of the present invention, as represented in the attachedfigures, is not intended to limit the scope of the invention as claimed,but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, reference throughout thisspecification to “certain embodiments,” “some embodiments,” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, appearances of the phrases“in certain embodiments,” “in some embodiments,” “in other embodiments,”or similar language throughout this specification do not necessarily allrefer to the same group of embodiments and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

It should be noted that reference throughout this specification tofeatures, advantages, or similar language does not imply that all of thefeatures and advantages that may be realized with the present inventionshould be or are in any single embodiment of the invention. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment ofthe present invention. Thus, discussion of the features and advantages,and similar language, throughout this specification may, but do notnecessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

1. A non-transitory computer-readable medium storing a computer program,the computer program configured to cause at least one processor to:receive a selection of a source; receive a selection of a target; callone or more artificial intelligence/machine learning (AI/ML) models thathave been trained to perform semantic matching between labels in thesource and labels in the target, between values in the source and thelabels in the target, or both; and copy values from the source to thetarget based on the semantic matching between the labels in the sourceand the labels in the target, between the values in the source and thelabels in the target, or both.
 2. The non-transitory computer-readablemedium of claim 1, wherein the computer program is further configured tocause the at least one processor to: generate one or more activities ina robotic process automation (RPA) workflow that copy data from thevalues in the source having labels that the one or more AI/ML modelsidentified as semantically matching the labels in the target intorespective fields or locations in the target.
 3. The non-transitorycomputer-readable medium of claim 2, wherein the computer program isfurther configured to cause the at least one processor to: generate anautomation implementing the one or more generated activities in the RPAworkflow; and deploy the generated automation and an RPA robotconfigured to execute the automation in a runtime environment.
 4. Thenon-transitory computer-readable medium of claim 1, wherein the computerprogram is further configured to cause the at least one processor to:determine a start of a copy-and-paste task; determine an end of thecopy-and-paste task; and generate associated activities for the startand the end of the copy-and-paste task.
 5. The non-transitorycomputer-readable medium of claim 1, wherein the computer program isfurther configured to cause the at least one processor to: receiveindications of fields or locations in the source associated withsemantically matched labels in the target and respective confidencescores from the one or more AI/ML models; and display the graphicalelements or locations associated with the semantically matched labels onthe target in a matching interface, display the respective confidencescores for the potential matching fields or locations identified by theone or more AI/ML models in the matching interface, or both.
 6. Thenon-transitory computer-readable medium of claim 1, wherein the computerprogram is further configured to cause the at least one processor to:receive a correction to a label, field, or location in the targetidentified by the one or more AI/ML models as having an associatedsemantically matching label in the source, receive an indication of anew label, field, or location in the target that was not semanticallymatched to a label in the source by the one or more AI/ML models, orboth; collect information pertaining to the corrected and/or newlylabeled label, field, or location in the target and the associated labelin the source; and directly or indirectly store the collectedinformation for retraining of the one or more AI/ML models.
 7. Thenon-transitory computer-readable medium of claim 1, wherein the computerprogram is further configured to cause the at least one processor to:generate a composite confidence score from the confidence scores for thefields or locations associated with semantically matched labels in thetarget; and display the composite confidence score in the matchinginterface.
 8. The non-transitory computer-readable medium of claim 1,wherein the one or more AI/ML models are trained by providing words andphrases with semantic associations between the words and phrases suchthat similar words and phrases for a given word or phrase can beidentified, and providing contextual labels pertaining to a screen inwhich the words and phrases appear.
 9. The non-transitorycomputer-readable medium of claim 1, wherein the computer program is orcomprises a robotic process automation (RPA) designer application. 10.The non-transitory computer-readable medium of claim 1, wherein the oneor more AI/ML models comprise a computer vision (CV) model, an opticalcharacter recognition (OCR) model, a label matching model, and an inputdata matching model, the label matching model matches labels detected bythe OCR model with fields or locations detected by the CV model, and theinput data model receives the matching labels from the label matchingmodel and semantically matches the labels and values from the sourcewith the labels and fields or locations associated with the semanticallymatched labels on the target.
 11. The non-transitory computer-readablemedium of claim 1, wherein the computer program is or comprises arobotic process automation (RPA) robot.
 12. The non-transitorycomputer-readable medium of claim 1, wherein the computer program isfurther configured to cause the at least one processor to: display atype selection interface providing options for supported types for thesource, the target, or both.
 13. The non-transitory computer-readablemedium of claim 1, wherein the source comprises content from a clipboardof an operating system.
 14. The non-transitory computer-readable mediumof claim 1, wherein the computer program is further configured to causethe at least one processor to: select at least one additional source;and provide information from the at least one additional source to theone or more AI/ML models in addition to information from the source toperform the semantic matching.
 15. A computer-implemented method,comprising: calling, by a computing system, one or more artificialintelligence/machine learning (AI/ML) models that have been trained toperform semantic matching between labels in a source and labels in atarget, between values in the source and the labels in the target, orboth; and copying values from the source to the target, by the computingsystem, based on the semantic matching between the labels in the sourceand the labels in the target, between the values in the source and thelabels in the target, or both.
 16. The computer-implemented method ofclaim 15, further comprising: generating, by the computing system, oneor more activities in a robotic process automation (RPA) workflow thatcopy data from the values in the source having labels that the one ormore AI/ML models identified as semantically matching the labels in thetarget into respective fields or locations in the target.
 17. Thecomputer-implemented method of claim 16, further comprising: generatingan automation implementing the one or more generated activities in theRPA workflow, by the computing system; and deploying the generatedautomation and an RPA robot configured to execute the automation in aruntime environment, by the computing system.
 18. Thecomputer-implemented method of claim 15, further comprising:determining, by the computing system, a start of a copy-and-paste task;determining, by the computing system, an end of the copy-and-paste task;and generating, by the computing system, associated activities for thestart and the end of the copy-and-paste task.
 19. Thecomputer-implemented method of claim 15, further comprising: receivingindications of fields or locations in the source associated withsemantically matched labels in the target and respective confidencescores from the one or more AI/ML models, by the computing system; anddisplaying, by the computing system, the graphical elements or locationsassociated with the semantically matched labels on the target in amatching interface, displaying the respective confidence scores for thepotential matching fields or locations identified by the one or moreAI/ML models in the matching interface, or both.
 20. Thecomputer-implemented method of claim 15, further comprising: receiving,by the computing system, a correction to a label, field, or location inthe target identified by the one or more AI/ML models as having anassociated semantically matching label in the source, receiving anindication of a new label, field, or location in the target that was notsemantically matched to a label in the source by the one or more AI/MLmodels, or both; collecting information pertaining to the correctedand/or newly labeled label, field, or location in the target and theassociated label in the source, by the computing system; and directly orindirectly storing the collected information for retraining of the oneor more AI/ML models, by the computing system.
 21. Thecomputer-implemented method of claim 15, further comprising: generatinga composite confidence score from the confidence scores for the fieldsor locations associated with semantically matched labels in the target,by the computing system; and displaying the composite confidence scorein the matching interface, by the computing system.
 22. Thecomputer-implemented method of claim 15, wherein the one or more AI/MLmodels are trained by providing words and phrases with semanticassociations between the words and phrases such that similar words andphrases for a given word or phrase can be identified, and providingcontextual labels pertaining to a screen in which the words and phrasesappear.
 23. The computer-implemented method of claim 15, wherein the oneor more AI/ML models comprise a computer vision (CV) model, an opticalcharacter recognition (OCR) model, a label matching model, and an inputdata matching model, the label matching model matches labels detected bythe OCR model with fields or locations detected by the CV model, and theinput data model receives the matching labels from the label matchingmodel and semantically matches the labels and values from the sourcewith the labels and fields or locations associated with the semanticallymatched labels on the target.
 24. The computer-implemented method ofclaim 15, further comprising: displaying, by the computing system, atype selection interface providing options for supported types for thesource, the target, or both.
 25. The computer-implemented method ofclaim 15, wherein the source comprises content from a clipboard of anoperating system.
 26. The computer-implemented method of claim 15,wherein the one or more AI/ML models use information from a plurality ofsources to perform the semantic matching between labels in the pluralityof sources and the labels in the target, between the values in theplurality of sources and the labels in the target, or both.
 27. Acomputing system, comprising: memory storing computer programinstructions; and at least one processor configured to execute thecomputer program instructions, wherein the computer program instructionsare configured to cause the at least one processor to: call one or moreartificial intelligence/machine learning (AI/ML) models that have beentrained to perform semantic matching between labels in the source andlabels in the target, between values in the source and the labels in thetarget, or both; and copy values from the source to the target based onthe semantic matching between the labels in the source and the labels inthe target, between the values in the source and the labels in thetarget, or both, wherein the computer program instructions are orcomprise a robotic process automation (RPA) designer application or anRPA robot.
 28. The computing system of claim 27, wherein the computerprogram instructions are further configured to cause the at least oneprocessor to: generate one or more activities in an RPA workflow thatcopy data from the values in the source having labels that the one ormore AI/ML models identified as semantically matching the labels in thetarget into respective fields or locations in the target.
 29. Thecomputing system of claim 27, wherein the computer program instructionsare further configured to cause the at least one processor to: generatean automation implementing the one or more generated activities in theRPA workflow; and deploy the generated automation and an RPA robotconfigured to execute the automation in a runtime environment.
 30. Thecomputing system of claim 27, wherein the computer program instructionsare further configured to cause the at least one processor to: determinea start of a copy-and-paste task; determine an end of the copy-and-pastetask; and generate associated activities for the start and the end ofthe copy-and-paste task.
 31. The computing system of claim 27, whereinthe computer program instructions are further configured to cause the atleast one processor to: receive indications of fields or locations inthe source associated with semantically matched labels in the target andrespective confidence scores from the one or more AI/ML models; anddisplay the graphical elements or locations associated with thesemantically matched labels on the target in a matching interface,display the respective confidence scores for the potential matchingfields or locations identified by the one or more AI/ML models in thematching interface, or both.
 32. The computing system of claim 27,wherein the computer program instructions are further configured tocause the at least one processor to: receive a correction to a label,field, or location in the target identified by the one or more AI/MLmodels as having an associated semantically matching label in thesource, receive an indication of a new label, field, or location in thetarget that was not semantically matched to a label in the source by theone or more AI/ML models, or both; collect information pertaining to thecorrected and/or newly labeled label, field, or location in the targetand the associated label in the source; and directly or indirectly storethe collected information for retraining of the one or more AI/MLmodels.
 33. The computing system of claim 27, wherein the computerprogram instructions are further configured to cause the at least oneprocessor to: generate a composite confidence score from the confidencescores for the fields or locations associated with semantically matchedlabels in the target; and display the composite confidence score in thematching interface.
 34. The computing system of claim 27, wherein theone or more AI/ML models are trained by providing words and phrases withsemantic associations between the words and phrases such that similarwords and phrases for a given word or phrase can be identified, andproviding contextual labels pertaining to a screen in which the wordsand phrases appear.
 35. The computing system of claim 27, wherein theone or more AI/ML models comprise a computer vision (CV) model, anoptical character recognition (OCR) model, a label matching model, andan input data matching model, the label matching model matches labelsdetected by the OCR model with fields or locations detected by the CVmodel, and the input data model receives the matching labels from thelabel matching model and semantically matches the labels and values fromthe source with the labels and fields or locations associated with thesemantically matched labels on the target.
 36. The computing system ofclaim 27, wherein the computer program instructions are furtherconfigured to cause the at least one processor to: display a typeselection interface providing options for supported types for thesource, the target, or both.
 37. The computing system of claim 27,wherein the source comprises content from a clipboard of an operatingsystem.
 38. The computing system of claim 27, wherein the one or moreAI/ML models use information from a plurality of sources to perform thesemantic matching between labels in the plurality of sources and thelabels in the target, between the values in the plurality of sources andthe labels in the target, or both.